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Record W4406679146 · doi:10.1177/23743735251314620

Understanding Patient Registries for Diabetes: A Scoping Review of Published Literature

2025· review· en· W4406679146 on OpenAlexafffund
Lana Moayad, Paige Alliston, Saira Khalid, Donna Fitzpatrick‐Lewis, Hertzel C. Gerstein, Diana Sherifali

Bibliographic record

VenueJournal of Patient Experience · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsMcMaster University Medical CentreHamilton Health SciencesPopulation Health Research InstituteMcMaster University
FundersMcMaster UniversityHamilton Health Sciences
KeywordsMedicineReferralGestational diabetesMEDLINEPsychological interventionContext (archaeology)Diabetes mellitusDiabetes managementType 2 diabetesFamily medicinePopulationNursingPregnancyEnvironmental health

Abstract

fetched live from OpenAlex

Background: Diabetes registries have grown in prevalence and incorporated patient engagement opportunities to support diabetes management. We aimed to understand the goals, purpose, and context for diabetes registries defined as patient-focused and how people with diabetes are engaging with these registries. Methods: We searched Pubmed, MEDLINE, Embase, and Emcare using the following criteria: (1) the population is people with diabetes mellitus, including type 1, type 2, and/or gestational diabetes; and (2) the study describes a patient focused registry. Results: The search identified 346 citations, 9 of which were included. The goals of the registries included: developing referral systems, evaluating community-based interventions, collecting self-reported data, improving access to care, and fostering diabetes communities. The delivery settings were community-based, outpatient, or primary care. The methods of delivery and level of patient engagement varied between registries. Conclusions: This scoping review identified 9 diabetes registries, with varying goals, purposes and levels of patient engagement. It highlights a need for registries centered on people with diabetes to promote engagement and facilitate long-term diabetes self-management.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.234
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.102
GPT teacher head0.391
Teacher spread0.289 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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