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Record W4416171665 · doi:10.1177/18333583251389777

Minimum dataset for the development of the National Haemophilia Registry

2025· article· en· W4416171665 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Information Management Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersIran University of Medical Sciences
KeywordsHaemophiliaData qualityEpidemiologyQuality (philosophy)Quality managementPatient registryMEDLINEData collectionWork (physics)

Abstract

fetched live from OpenAlex

BACKGROUND: Haemophilia is a lifelong and chronic disease that has adverse consequences for the patient. The haemophilia registry is a key tool for managing this disease. OBJECTIVE: The present study aimed to design a minimum dataset for developing a registry system for haemophilia. METHOD: This study was conducted in two stages. In the first stage, in order to conduct a scoping review, PubMed, Scopus and Web of Science databases were searched using relevant keywords up to 4 July 2025. The study selection process was based on the PRISMA guidelines, and finally, 40 articles were included. In the second stage, the data items retrieved from the studies were evaluated and consulted by 14 haematology specialists through a questionnaire. The minimum data items for haemophilia registry were confirmed based on the level of agreement of the participants (more than 75%), and descriptive statistics were used for data analysis, which was performed using the SPSS software (IBM Corp., Armonk, NY, USA). RESULTS: The initial minimum data items for the haemophilia registry system were extracted from 40 studies. These items included 77 items in 4 main categories: demographic data (21 items), laboratory data (32 items), clinical data (21 items) and adverse outcomes (3 items). Finally, these data items were validated by 14 haematology specialists. In the final dataset, 58 items, distributed across 4 categories, achieved an agreement of more than 75%, comprising 8 demographic items, 28 laboratory items, 17 clinical items and 3 adverse outcome items. CONCLUSION: Registries record different data according to their purposes. The importance of this work lies in providing a minimum dataset for registering haemophilia patients in Iran, which can help improve the quality of care, facilitate future research and align with international registry systems for bleeding diseases. Therefore, the findings of this study provide a basis for designing, implementing and improving the haemophilia registry system in Iran.Implications for health information management practice:The findings of this study provide a strong foundation for designing and implementing a National Haemophilia Registry in Iran. This system will standardise and integrate data, prevent duplicate records and enhance treatment planning. It will also support epidemiological and clinical research with links to international databases, while improving patient care, follow-up and reducing complications. Overall, it can help align Iran with global standards for managing bleeding disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.399
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it