Specialist-Led Diabetes Registries and Prevalence of Poor Glycemic Control in Type 2 Diabetes: The Diabetes Registry Outcomes Project for A1C Reduction (DROP A1C)
Bibliographic record
Abstract
OBJECTIVE: To highlight the utility of a large patient registry to identify functionally refractory patients (persistent HbA1c ≥75 mmol/mol [9.0%]) with type 2 diabetes, identify their barriers to glycemic control, and implement barrier-specific care path strategies to improve glycemic control. RESEARCH DESIGN AND METHODS: A working group developed a structured tool to optimize the collection of information on barriers to glycemic control and designed structured care paths to address each barrier. Participants were identified from a large Canadian registry and were assigned to a certified diabetes educator (CDE) as their case manager for a 12-month period to coordinate assessment of their barriers and to implement appropriate care path strategies. The primary outcome measure was the mean change in HbA1c from baseline at 12 months. RESULTS: Overall, 3,662 refractory patients were initially identified of whom 1,379 were eligible for inclusion and 155 enrolled. The most common barrier categories participants identified were psychological/support (93%), socioeconomic (87%), and accessibility (82%), with high concordance (75-94%) between participant and CDE. No specific barriers were predictive of hyperglycemia. After implementation of barrier-specific care paths, the mean reduction in HbA1c at 12 months was 17 mmol/mol (1.5%; P < 0.01 vs. baseline) versus only 5 mmol/mol (0.5%) in the source cohort (n = 966) who continued with standard care. The incidence of severe hypoglycemia did not change significantly during the study. CONCLUSIONS: In registry-identified hyperglycemic patients with type 2 diabetes, the use of barrier-specific care paths significantly improved glycemic control in otherwise refractory patients with persistently elevated HbA1c. Further studies using this strategy in other practice settings are warranted.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".