Diabetes Registries: Where We Are and Where Are We Headed?
Bibliographic record
Abstract
Disease registries are a searchable list of all patients with a particular chronic condition that often interface with an electronic medical record. The well-designed registry links all members of the patient's health team and provides key information for patients and physicians. The critical impact of a registry is that it can allow timely identification of high-risk subpopulations permitting the health care team to intensify treatment. Diabetes is a data-driven disease that lends itself well to registry use. This review will examine some current registry uses and highlight some of the respective challenges and benefits. This review compares key examples of registries in different health settings. These include a municipal registry (New York City), academic health centers (Penn State Milton S. Hershey Medical Center), third-party payers (Kaiser Permanente), the Veterans Affairs Health System, and international registries (the DIABCARE Q-NET in Europe and the National Diabetes Surveillance System in Canada). Different aspects are compared and contrasted such as the institutional plan for each and whether care in the "here and now" is impacted.
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 | 0.066 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.024 | 0.063 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".