Understanding Patient Registries for Diabetes: A Scoping Review of Published Literature
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".