Patient Age, Ethnicity, Medical History, and Risk Factor Profile, but Not Drug Insurance Coverage, Predict Successful Attainment of Glycemic Targets
Bibliographic record
Abstract
OBJECTIVE: To identify factors in patients with type 2 diabetes and A1C >7.0% associated with attainment of A1C ≤ 7.0%. RESEARCH DESIGN AND METHODS: We used a prospective registry of 5,280 Canadian patients in primary care settings enrolled in a 12-month glycemic pharmacotherapy optimization strategy based on national guidelines. RESULTS: At close out, median A1C was 7.1% (vs. 7.8% at baseline) with 48% of subjects achieving A1C ≤ 7.0% (P < 0.0001). Older patients of Asian or black origin, those with longer diabetes duration, those with lower baseline A1C, BMI, LDL cholesterol, and blood pressure, and those on angiotensin receptor blockers and a lower number of antihyperglycemic agents, were more likely to achieve A1C ≤ 7.0% at some point during the study (all P < 0.0235). Access to private versus public drug coverage did not impact glycemic target realization. CONCLUSIONS: Patient demography, cardiometabolic health, and ongoing pharmacotherapy, but not access to private drug insurance coverage, contribute to the care gap in type 2 diabetes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".