JMIR Med Inform. 2025 Feb 28;13:e67984. doi: 10.2196/67984.

ABSTRACT

BACKGROUND: The digitalization of health care has increased the demand for efficient data exchange, emphasizing semantic interoperability. SNOMED Clinical Terms (SNOMED CT), a comprehensive terminology with over 360,000 medical concepts, supports this need. However, it cannot cover all medical scenarios, particularly in complex cases. To address this, SNOMED CT allows postcoordination, where users combine precoordinated concepts with new expressions. Despite SNOMED CT's potential, the creation and validation of postcoordinated expressions (PCEs) remain challenging due to complex syntactic and semantic rules.

OBJECTIVE: This work aims to develop a tool that validates postcoordinated SNOMED CT expressions, focusing on providing users with detailed, automated correction instructions for syntactic and semantic errors. The goal is not just validation, but also offering user-friendly, actionable suggestions for improving PCEs.

METHODS: A tool was created using the Fast Healthcare Interoperability Resource (FHIR) service $validate-code and the terminology server Ontoserver to check the correctness of PCEs. When errors are detected, the tool processes the SNOMED CT Concept Model in JSON format and applies predefined error categories. For each error type, specific correction suggestions are generated and displayed to users. The key added value of the tool is in generating specific correction suggestions for each identified error, which are displayed to the users. The tool was integrated into a web application, where users can validate individual PCEs or bulk-upload files. The tool was tested with real existing PCEs, which were used as input and validated. In the event of errors, appropriate error messages were generated as output.

RESULTS: In the validation of 136 PCEs from 304 FHIR Questionnaires, 18 (13.2%) PCEs were invalid, with the most common errors being invalid attribute values. Additionally, 868 OncoTree codes were evaluated, resulting in 161 (20.9%) PCEs containing inactive concepts, which were successfully replaced with valid alternatives. A user survey reflects a favorable evaluation of the tool's functionality. Participants found the error categorization and correction suggestions to be precise, offering clear guidance for addressing issues. However, there is potential for enhancement, particularly regarding the level of detail in the error messages.

CONCLUSIONS: The validation tool significantly improves the accuracy of postcoordinated SNOMED CT expressions by not only identifying errors but also offering detailed correction instructions. This approach supports health care professionals in ensuring that their PCEs are syntactically and semantically valid, enhancing data quality and interoperability across systems.

PMID:40019788 | DOI:10.2196/67984

BMC Nurs. 2025 Feb 25;24(1):210. doi: 10.1186/s12912-025-02847-x.

ABSTRACT

INTRODUCTION: Enhancing clinical outcomes and patient satisfaction can be achieved through interprofessional collaboration between physicians and nurses. Conversely, a lack of nurse-physician interprofessional collaboration compromises patient safety, care, and improvement, and creates moral discomfort for healthcare professionals. Studies indicate that failures in interprofessional collaboration between nurses and physicians lead to adverse medical events, including hospital-acquired infections, medication administration errors, and unnecessary health-related costs.

OBJECTIVE: This systematic review and meta-analysis aimed to investigate the pooled proportions of the interprofessional collaborations towards patient care and associated factors among nurses and physicians in Ethiopia, 2024.

METHODS: A comprehensive search was conducted to find articles on interprofessional collaboration towards patient care and associated factors among nurses and physicians in Ethiopia. The study included cross-sectional studies conducted in Ethiopia and published in English from inception up to August 20, 2024. Excluded were conference proceedings, qualitative research, commentaries, editorial letters, case reports, case series, and monthly and annual police reports. The search encompassed full-text publications written in English and databases such as PubMed/MEDLINE, African Journals Online (AJOL), Semantic Scholar, Google Scholar, and Google. A checklist from the Joanna Briggs Institute (JBI) was used to evaluate the quality of the studies. Two independent reviewers performed data extraction, critical appraisal, and article screening. Statistical analysis was performed using STATA-17 software. A random-effects model was employed to estimate pooled proportions, and effect sizes with 95% confidence intervals were used to analyze determinants of interprofessional collaboration in patient care among nurses and physicians. Funnel plots and Egger's test were used to examine the possibility of publication bias (p-value < 0.10), and the trim-and-fill method by Duval and Tweedie was applied to adjust for publication bias.

RESULTS: Five studies with a total of 1686 study participants that are conducted in three Ethiopian regions and meet the inclusion criteria were reviewed and pooled for this evaluation. The pooled proportions of the interprofessional collaboration towards patient care in Ethiopia is 52.73% (95% CI = 44.66, 60.79%, I2 = 91.5%). Factors such as attitude (favorable attitude towards collaboration) (OR = 1.13, 95% CI: 0.13, 9.89, I2 = 97.7%) and organizational support (satisfaction towards organizational support) (OR = 0.38, 95% CI: 0.07, 2.10, I2 = 97.5%) were not significantly associated with interprofessional collaboration towards patient care.

CONCLUSION: In summary, this systematic review and meta-analysis reveal that interprofessional collaboration between nurses and physicians in Ethiopia is moderately common, with a pooled proportion of 52.73%. This finding underscores the need for ongoing efforts to enhance collaborative practices to further improve patient care outcomes. Additionally, the review identified two potential contributors to interprofessional collaboration: satisfaction with organizational support and favorable attitudes towards collaboration. However, the pooled effects of these factors did not show a significant association with interprofessional collaboration. This highlights the necessity for further primary research to identify additional factors that may influence interprofessional collaboration and enhance patient care outcomes. Notable limitations of this study include significant variation among studies, a small number of studies, a focus solely on public hospitals, restriction to English-language publications, only observational studies, and limited access to databases such as EMBASE, CINAHL, and Web of Science.

REGISTRATION: This systematic review and meta-analysis was registered in Prospero with the registration ID and link as follows: CRD42024579370; https://www.crd.york.ac.uk/prospero/#recordDetails .

PMID:40001025 | DOI:10.1186/s12912-025-02847-x

JMIR Nurs. 2025 Feb 21;8:e60810. doi: 10.2196/60810.

ABSTRACT

BACKGROUND: The digitalization of health care in Germany holds great potential to improve patient care, resource management, and efficiency. However, strict data protection regulations, fragmented infrastructures, and resistance to change hinder progress. These challenges leave care institutions reliant on outdated paper-based workflows, particularly for patient data transmission, despite the pressing need for efficient tools to support health care professionals amid a nursing shortage and rising demand for care.

OBJECTIVE: This paper aims to analyze Germany's care transition record (CTR) and CTR transmission process as part of transition management and suggests improvements toward a seamless digital solution.

METHODS: To understand the current challenges of manual CTR transfers, we used a mixed methods approach, which included a web-based questionnaire with nursing professionals, field observations, business process model and notation modeling, semantic and frequency analysis of CTR entries, and user story mapping.

RESULTS: A web-based questionnaire involving German nursing professionals (N=59) revealed considerable delays in patient care due to manual, patient-transferred CTRs. Of the 33 usable responses (n=33), 70% (n=23) of the respondents advocating for digital transmission to improve efficiency. Observations (N=11) in care facilities (n=5, 45%) and a hospital (n=6, 55%) confirmed the high administrative burden, averaging 34.67 (SD 10.78) minutes per CTR within a hospital and 44.6 (SD 20.5) minutes in care facilities. A semantic analysis of various CTRs (N=4) highlighted their differences and complexity, stressing the need for standardization. Analyzing a new CTR standard (care information object CTR) and manually mapping an existing CTR to it showed that the procedure was ambiguous, and some associations remained unclear. A frequency analysis of CTR entities revealed which were most used. In addition, discussions with care staff pointed out candidates for the most relevant entities. On the basis of the key findings, a stepwise transition approach toward a road map proposal for a standardized, secure transfer of CTRs was conceptualized. This road map in the form of a user story map, encompassing a "CTR transformer" (mapping of traditional CTRs to a new standard) and "care information object CTR viewer/editor" (in short, CIO-CTR viewer and editor; a new standard for viewing, editing, and exporting), shows a possibility to bridge the transition time until all institutions fully support the new standard.

CONCLUSIONS: A future solution should simplify the overall CTR transmission process by minimizing manual transfers into in-house systems, standardizing the CTR, and providing a secure digital transfer. This could positively impact the overall care process and patient experience. With our solutions, we attempt to support care staff in their daily activities and processes until nationwide state regulations are implemented successfully, though the timeline for this remains uncertain.

PMID:39982779 | DOI:10.2196/60810

medRxiv [Preprint]. 2025 Feb 2:2025.01.31.25321503. doi: 10.1101/2025.01.31.25321503.

ABSTRACT

We developed a simulated process to show a software implementation to facilitate an approach to integrate the Informed Consent Ontology, a reference ontology of informed consent information, to express implicit description and implement conceptual permission from informed consent life cycle. An early study introduced an experimental method to use Semantic Web Rule Language (SWRL) to describe and represent permissions to computational deduce more information from the Informed Consent Ontology (ICO), demonstrated by the use of the All of Us informed consent documents. We show how incomplete information in informed consent documents can be elucidated using a computational model of permissions toward health information technology that integrates ontologies. Future goals entail applying our computational approach for specific sub-domains of the informed consent life cycle, specifically for vaccine informed consent.

PMID:39974098 | PMC:PMC11838618 | DOI:10.1101/2025.01.31.25321503

Sci Data. 2025 Feb 17;12(1):282. doi: 10.1038/s41597-025-04580-1.

ABSTRACT

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

PMID:39962095 | DOI:10.1038/s41597-025-04580-1

J Biomed Inform. 2025 Feb 15:104804. doi: 10.1016/j.jbi.2025.104804. Online ahead of print.

ABSTRACT

OBJECTIVE: Visual representations generally serve as supplements to information, rather than as bearers of computable information themselves. Our objective is to develop a method for creating semantically-augmented graphic libraries that will serve as visual standards and can be implemented as visual assets in intelligent information systems.

METHODS: Graphics were developed using a composable approach and specified using SVG. OWL was used to represent the entities of our system, which include elements, units, graphics, graphic libraries, and library collections. A graph database serves as our data management system. Semantics are applied at multiple levels: (a) each element is associated with a semantic style class to link visual style to semantic meaning, (b) graphics are described using object properties and data properties, (c) relationships are specified between graphics, and (d) mappings are made between the graphics and outside resources.

RESULTS: The Graphic Library web application enables users to browse the libraries, view information pages for each graphic, and download individual graphics. We demonstrate how SPARQL can be employed to query the graphics database and the APIs can be used to retrieve the graphics and associated data for applications. In addition, this work shows that our method of designing composable graphics is well-suited to depicting variations in human anatomy.

CONCLUSION: This work provides a bridge between visual communication and the field of knowledge representation. We demonstrate a method for creating visual standards that are compatible with practices in biomedical ontology and implement a system for making them accessible to information systems.

PMID:39961540 | DOI:10.1016/j.jbi.2025.104804

J Biomed Inform. 2025 Feb 12:104788. doi: 10.1016/j.jbi.2025.104788. Online ahead of print.

ABSTRACT

BACKGROUND: If hospital Clinical Data Warehouses are to address today's focus in personalized medicine, they need to be able to track patients longitudinally and manage the large data sets generated by whole genome sequencing, RNA analyses, and complex imaging studies. Current Clinical Data Warehouses address neither issue. This paper reports on methods to enrich current systems by providing provenance data allowing patient histories to be followed longitudinally and managing the linking and versioning of large data sets from whatever source. The methods are open source and applicable to any clinical data warehouse system, whether data schema it uses.

METHOD: We introduce gitOmmix, an approach that overcomes these limitations, and illustrate its usefulness in the management of medical omics data. gitOmmix relies on (i) a file versioning system: git, (ii) an extension that handles large files: git-annex, (iii) a provenance knowledge graph: PROV-O, and (iv) an alignment between the git versioning information and the provenance knowledge graph.

RESULTS: Capabilities inherited from git and git-annex enable retracing the history of a clinical interpretation back to the patient sample, through supporting data and analyses. In addition, the provenance knowledge graph, aligned with the git versioning information, enables querying and browsing provenance relationships between these elements.

CONCLUSION: gitOmmix adds a provenance layer to CDWs, while scaling to large files and being agnostic of the CDW system. For these reasons, we think that it is a viable and generalizable solution for omics clinical studies.

PMID:39952627 | DOI:10.1016/j.jbi.2025.104788

Front Robot AI. 2025 Jan 29;11:1363150. doi: 10.3389/frobt.2024.1363150. eCollection 2024.

ABSTRACT

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific computational building blocks that prevent code duplication and enable robots to adapt their software themselves. The envisaged algorithms are numerical solvers based on graph structures. In this article, we focus on kinematics and dynamics algorithms, but examples such as message passing on probabilistic networks and factor graphs or cascade control diagrams fall under the same pattern. The tools rely on mature standards from the Semantic Web. They first synthesize algorithms symbolically, from which they then generate efficient code. The use case is an overactuated mobile robot with two redundant arms.

PMID:39944358 | PMC:PMC11813742 | DOI:10.3389/frobt.2024.1363150

Stroke. 2025 Mar;56(3):716-724. doi: 10.1161/STROKEAHA.124.047893. Epub 2025 Feb 5.

ABSTRACT

BACKGROUND: Communicative ability after stroke influences patient outcomes. Limited research has explored the impact of aphasia when it intersects with cultural or linguistic differences on receiving stroke care and patient outcomes. We investigated associations between requiring an interpreter and the provision of evidence-based stroke care and outcomes for people with aphasia in the inpatient rehabilitation setting.

METHODS: Retrospective patient-level data from people with aphasia were aggregated from the Australian Stroke Foundation National Stroke Audit-Rehabilitation Services (2016-2020). Multivariable regression models compared adherence to processes of care (eg, home assessment complete, type of aphasia management) and in-hospital outcomes (eg, length of stay, discharge destination) by the requirement of an interpreter. Outcome models were adjusted for sex, stroke type, hospital size, year, and stroke severity factors.

RESULTS: Among 3160 people with aphasia (median age, 76 years; 56% male), 208 (7%) required an interpreter (median age, 77 years; 52% male). The interpreter group had a more severe disability on admission, reflected by reduced cognitive (6% versus 12%, P=0.009) and motor Functional Independence Measure scores (6% versus 12%, P=0.010). The interpreter group were less likely to have phonological and semantic interventions for their aphasia (odds ratio, 0.57 [95% CI, 0.40-0.80]) compared with people not requiring an interpreter. They more often had a carer (68% versus 48%, P<0.001) and were more likely to be discharged home with supports (odds ratio, 1.48 [95% CI, 1.08-2.04]). The interpreter group had longer lengths of stay (median 31 versus 26 days, P=0.005).

CONCLUSIONS: Some processes of care and outcomes differed in inpatient rehabilitation for people with poststroke aphasia who required an interpreter compared with those who did not. Equitable access to therapy is imperative and greater support for cultural/linguistic minorities during rehabilitation is indicated.

PMID:39907026 | DOI:10.1161/STROKEAHA.124.047893

J Am Med Inform Assoc. 2025 Jan 31:ocaf012. doi: 10.1093/jamia/ocaf012. Online ahead of print.

ABSTRACT

OBJECTIVES: Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care.

MATERIALS AND METHODS: myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology.

RESULTS: Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface.

DISCUSSION: The ability to search and explore myAURA's heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support.

CONCLUSION: Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.

PMID:39890454 | DOI:10.1093/jamia/ocaf012

JMIR Res Protoc. 2025 Jan 29;14:e63597. doi: 10.2196/63597.

ABSTRACT

BACKGROUND: TheKeep.Ca was built to facilitate engagement with those experiencing cancer in Manitoba, Canada. Constructed between 2020 and 2024 with a group of patient advisors, the website includes information on engagement activities including research participation, the patient advisor role, and how those experiencing cancer can access these Manitoba activities. A link allows visitors to register to be contacted about activities that match their demographics, cancer history, and activity preferences. After TheKeep.Ca was constructed, this protocol was developed to establish TheKeep.Ca as a platform for scientific research focused on optimally engaging those experiencing cancer.

OBJECTIVE: We asked the following questions: (1) What was the patient advisors' experience who participated in developing TheKeep.Ca? (2) What are the baseline characteristics of website traffic and registrants at TheKeep.Ca? (3) How does registering with TheKeep.Ca impact the cancer experience?

METHODS: The planned launch date for the website and initiation of research activities is January 2025. For objective 1, the active patient advisors (N=6) participating in the website project will be invited to participate in project activities including with responses to a question prompt sheet, semistructured audio-recorded interviews, or both. Responses and interviews will be analyzed using reflexive thematic analysis to understand and inform practices for patient engagement on projects. At the website launch, TheKeep.Ca will become publicly accessible and indexable on internet search engines, but no additional promotional interventions will take place in the initial 6 months resulting in visitors primarily from web search traffic. For objective 2, Google Analytics and website registrant data collected during the first six months will be analyzed to obtain baseline characteristics of website visitors. For objective 3, an online survey will be emailed to registrants six months after the website launch characterizing their website experience, the activities they participated in, and collecting feedback on the website. For objectives 2 and 3, quantitative data will be analyzed using both descriptive and inferential statistics, and qualitative data from open-ended questions will be analyzed using thematic analysis guided by an inductive descriptive semantic approach.

RESULTS: This study was approved by the University of Manitoba Health Research Ethics Board on December 12, 2024 (HS26614-H2024L263). Institutional approval from CancerCare Manitoba is pending as of December 23, 2024. Findings from objective 1 are expected to be finalized within the first six months after the website launch. Those from objectives 2 and 3 are expected by the 12-month mark. Reporting will include peer-reviewed journals, conferences, and a lay-language summary on TheKeep.Ca.

CONCLUSIONS: The research outlined in this protocol will facilitate understanding patient advisors' experience in developing TheKeep.Ca. It will also characterize the website' effectiveness and its impact on the cancer experience, providing a baseline and direction for future research and development.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63597.

PMID:39879620 | DOI:10.2196/63597

J Biomed Inform. 2025 Jan 23:104761. doi: 10.1016/j.jbi.2024.104761. Online ahead of print.

ABSTRACT

OBJECTIVE: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.

METHODS: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer's disease patients.

RESULTS: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API3. ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms' performance. Notably, it successfully categorized Alzheimer's patients into two subgroups with varying mortality rates.

CONCLUSION: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

PMID:39863245 | DOI:10.1016/j.jbi.2024.104761

Cortex. 2024 Dec 27;184:172-193. doi: 10.1016/j.cortex.2024.11.019. Online ahead of print.

ABSTRACT

Retrieving words quickly and correctly is an important language competence. Semantic contexts, such as prior naming of categorically related objects, can induce conceptual priming but also lexical-semantic interference, the latter likely due to enhanced competition during lexical selection. In the continuous naming (CN) paradigm, such semantic interference is evident in a linear increase in naming latency with each additional member of a category out of a seemingly random sequence of pictures being named (cumulative semantic interference/CSI effect). Extensively studied in neurotypical participants, CSI studies in people with aphasia (PWA) are rare, although some lesions regularly and persistently impair word retrieval. In the present study, 20 PWA with lesions in the extended left hemispheric language network and 20 matched controls underwent a CN paradigm, naming photographs of closely related objects from 24 categories (e.g., birds) with 5 members each. The experiment was conducted web-based (Stark et al., 2022) on three days (day 1, 2, and 8). The main results are: (i) Mild-moderate aphasia does not preclude web-based testing. (ii) The CSI effect in naming latencies (∼21 ms per ordinal position) did not differ significantly between groups but was more variable in the PWA; the effect was stable across days. (iii) Overall response times decreased between day 1 and day 2, but remained stable on day 8. (iv) In PWA, increased error-rates paralleled the latency-based CSI effect, suggesting stronger interference in this group. (v) Exploratory analyses suggest that lesions in a large area, including frontal, inferior parietal, pre- and post-central opercular cortices, are linked to a larger CSI effect. At a more lenient statistical threshold, lesions in occipital and supramarginal cortices were associated with increased overall naming latencies. These results offer an initial step toward identifying the neuronal underpinnings of semantic context effects in PWA. We conclude that web-based assessment is feasible in PWA and yields a stable CSI effect over repetitive testing. While not directly clinically applicable, the findings could serve as a foundation for exploring training-interventions targeting lexical activation, interference resolution, or word selection.

PMID:39862560 | DOI:10.1016/j.cortex.2024.11.019

JMIR Res Protoc. 2025 Jan 23;14:e65137. doi: 10.2196/65137.

ABSTRACT

BACKGROUND: Injectable platelet-rich fibrin (i-PRF) has the capacity to release great amounts of several growth factors, as well as to stimulate increased fibroblast migration and the expression of collagen, transforming growth factor β, and platelet-derived growth factor. Consequently, i-PRF can be used as a bioactive agent to promote periodontal tissue regeneration.

OBJECTIVE: We aim to compare and evaluate the effectiveness of i-PRF in periodontal tissue regeneration.

METHODS: We will conduct an electronic search in the following databases: PubMed, Cochrane Library, Google Scholar, Semantic Scholar, Scopus, and Web of Science. Papers will be restricted to those in English and to those that are randomized controlled trials comparing PRF or any other biomaterial with i-PRF for periodontal regeneration during dental treatment. The included papers in this review and the reference lists of pertinent reviews will be manually searched. The selection of studies, data extraction, and assessment will be carried out separately by 2 reviewers using the Risk of Bias 2 tool for the included research.

RESULTS: The success of i-PRF will be evaluated by comparing the mean difference in periodontal regeneration of soft and hard tissues in terms of gingival recession, probing pocket depth, clinical attachment level, bone gain, and gingival width. The combined effect size measurements and the associated 95% CIs will be estimated using a random-effects model. The synthesis or work for this systematic review started in October 2023 and will last until December 2025.

CONCLUSIONS: i-PRF may play a role in dentistry and could enhance soft and hard tissue regeneration.

TRIAL REGISTRATION: PROSPERO CRD42023464250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=464250.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/65137.

PMID:39847766 | DOI:10.2196/65137

Front Nutr. 2025 Jan 6;11:1503389. doi: 10.3389/fnut.2024.1503389. eCollection 2024.

ABSTRACT

INTRODUCTION: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.

METHODS: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge. This research is a novel contribution because it allows for the interconnection and complementation of food composition data with knowledge and takes what has been done in the past a step further by enabling the integration of knowledge. NutriBase focuses on two important challenges; data (semantic) harmonization by using the existing ontologies, and reducing missing data by semi-automatic data imputation made from conflating with existing databases.

RESULTS AND DISCUSSION: The developed web-based tool is highly modifiable and can be further customized to meet national or international requirements. It can help create and maintain the quality management system needed to assure data quality. Newly generated data and knowledge can continuously be added, as interoperability with other systems is enabled. The tool is intended for use by domain experts, food compilers, and researchers who can add and edit food-relevant data and knowledge. However, the tool is also accessible to food manufacturers, who can regularly update information about their products and thus give consumers access to current data. Moreover, the traceability of the data and knowledge provenance allows the compilation of a trustworthy management system. The system is designed to allow easy integration of data from different sources, which enables data borrowing and reduction of missing data. In this paper, the feasibility of NutriBase is demonstrated on Slovenian food-related data and knowledge, which is further linked with international resources. Outputs such as matched food components and food classifications have been integrated into semantic resources that are currently under development in various international projects.

PMID:39834464 | PMC:PMC11743969 | DOI:10.3389/fnut.2024.1503389

J Clin Nurs. 2025 Jan 20. doi: 10.1111/jocn.17664. Online ahead of print.

ABSTRACT

AIM: To cross-culturally adapt a framework for person-centred leadership in residential care for older people in Sweden.

DESIGN: This study has an exploratory and descriptive design.

METHODS: The translation procedure followed a cyclic process of translation into Swedish and back-translation into English by two independent bilingual linguists. An evaluation of conceptual and semantic equivalence and comprehensiveness between the original English version and the translated Swedish version was performed by an expert committee. The translated version of the framework was validated by leaders (n = 34) in residential care, who assessed its relevance through a web form. The adaptation of the framework followed recommended guidelines for cross-cultural adaptation.

RESULTS: The translation procedure resulted in two minor changes related to the wording in two descriptors. The results of the validation procedure showed that the framework is relevant for leaders in Swedish residential care for older people.

CONCLUSION: The cross-culturally adapted framework is useful and suitable for leaders in Swedish residential care for older people. The framework clarifies the leader's role and identifies leadership attributes and requirements for person-centred leadership in residential care, thereby providing support to leaders by framing person-centred leadership.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The framework can be used as a guide for leadership training and/or development initiatives in residential care. It can be further extended to nursing curriculums, leadership development programs, and organisational performance and development processes. It may also provide a foundation for policy and guidelines by establishing the activities required for leaders to promote person-centredness in the care of older people.

REPORTING METHOD: This study followed the STROBE checklist for cross-sectional studies.

PATIENT AND PUBLIC CONTRIBUTION: There was no patient or public contribution.

PMID:39831575 | DOI:10.1111/jocn.17664

MethodsX. 2024 Dec 16;14:103092. doi: 10.1016/j.mex.2024.103092. eCollection 2025 Jun.

ABSTRACT

This study explores the possibility of integrating and retrieving heterogenous data across platforms by using ontology graph databases to enhance educational insights and enabling advanced data-driven decision-making. Motivated by some of the well-known universities and other Higher Education Institutions ontology, this study improvises the existing entities and introduces new entities in order to tackle a new topic identified from the preliminary interview conducted in the study to cover the study objective. The paper also proposes an innovative ontology, referred to as Student Performance and Course, to enhance resource management and evaluation mechanisms on course, students, and MOOC performance by the faculty. The model solves the issues of data accumulation and their heterogeneity, including the problem of having data in different formats and various semantic similarities, and is suitable for processing large amounts of data in terms of scalability. Thus, it also offers a way to confirm the process of data retrieval that is based on performance assessment with the help of an evaluation matrix.

PMID:39811619 | PMC:PMC11731703 | DOI:10.1016/j.mex.2024.103092

BMC Med Educ. 2025 Jan 12;25(1):52. doi: 10.1186/s12909-025-06644-7.

ABSTRACT

BACKGROUND: Despite the importance of effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning, unanswered questions remain. Understanding how these cognitive operations can be observed and assessed is crucial, particularly considering the rapid growth of artificial intelligence and its integration into health education. A scoping review was conducted to map the literature regarding educational strategies to support transformation and articulation of clinical data, the learning tasks expected of students when exposed to these strategies and methods used to assess individuals' proficiency METHODS: Based on the Joanna Briggs Institute methodology, the authors searched 5 databases (CINAHL, MEDLINE, EMBASE, PsycINFO and Web of Science), ProQuest Dissertations & Theses electronic database and Google Scholar. The data were synthesized narratively using descriptive statistics.

RESULTS: A total of 38 articles were included in the final synthesis. Most studies were conducted in North America and Europe (n = 30, 79%) focused primarily on medical students (n = 35, 92%) and mainly used observational (n = 17, 45%) or methodological (n = 8, 21%) designs. Various educational strategies were identified, the most common were resolution of written or computerized case-based scenarios (n = 13; 52%) and simulated or real patient encounters (n = 6; 24%). The learning tasks comprised, among others, identifying key findings, translating clinical information, synthesizing cases aloud, and writing a summary statement. Furthermore, the review included assessment methods and rubrics with assessment criteria for clinical data transformation and articulation. The narrative synthesis shows positive results when integrating various educational strategies within clinical reasoning curricula compared to a single strategy used episodically.

LIMITATIONS AND CONCLUSIONS: The varying objectives, diversity of educational strategies documented, and heterogeneity of the evaluation tools or rubrics limit our conclusions. However, insights gained will help educators develop effective approaches for teaching clinical reasoning. Additional research is needed to evaluate the impacts of educational strategies aimed at developing skills for the transformation and articulation of clinical data.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:39800713 | DOI:10.1186/s12909-025-06644-7

J Biomed Semantics. 2025 Jan 8;16(1):1. doi: 10.1186/s13326-024-00322-1.

ABSTRACT

BACKGROUND: TogoID ( https://togoid.dbcls.jp/ ) is an identifier (ID) conversion service designed to link IDs across diverse categories of life science databases. With its ability to obtain IDs related in different semantic relationships, a user-friendly web interface, and a regular automatic data update system, TogoID has been a valuable tool for bioinformatics.

RESULTS: We have recently expanded TogoID's ability to represent semantics between datasets, enabling it to handle multiple semantic relationships within dataset pairs. This enhancement enables TogoID to distinguish relationships such as "glycans bind to proteins" or "glycans are processed by proteins" between glycans and proteins. Additional new features include the ability to display labels corresponding to database IDs, making it easier to interpret the relationships between the various IDs available in TogoID, and the ability to convert labels to IDs, extending the entry point for ID conversion. The implementation of URL parameters, which reproduces the state of TogoID's web application, allows users to share complex search results through a simple URL.

CONCLUSIONS: These advancements improve TogoID's utility in bioinformatics, allowing researchers to explore complex ID relationships. By introducing the tool's multi-semantic and label features, TogoID expands the concept of ID conversion and supports more comprehensive and efficient data integration across life science databases.

PMID:39780290 | DOI:10.1186/s13326-024-00322-1

Sensors (Basel). 2024 Dec 20;24(24):8142. doi: 10.3390/s24248142.

ABSTRACT

Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches.

PMID:39771876 | DOI:10.3390/s24248142