Medication Adherence Affects Risk of New Diabetes Complications
Bibliographic record
Abstract
BACKGROUND: Previous outcomes-based studies of adherence to diabetes medications have focused on glycemic control and are limited by questions of temporality and uncontrolled confounding. OBJECTIVE: This retrospective cohort study of new oral antidiabetic medication users examined the effect of adherence on risk of incident macrovascular and microvascular complications. METHODS: A nationwide integrated insurance claims and laboratory database was used to identify new oral antidiabetic medication users between January 2004 and December 2009. People with preexisting complications were excluded and the remaining cohort was followed until development of a new diabetes complication or December 2010. Medication adherence was calculated at 3-month intervals and entered as a time-dependent variable in a Cox proportional hazards model. Covariables entered in the model included patient demographics, clinical laboratory data, a medical frailty indicator and a mortality risk score from the Johns Hopkins adjusted clinical groups system, and medication use at baseline. RESULTS: Among the 54 505 included patients, the median age was 60 years, 28 125 (52%) were men, 1447 (3%) were considered frail, the mean mortality risk score was 33.7 (±11.1), and 9793 (18%) developed a new diabetes complication. Good adherence (medication possession ratio ≥0.8) was associated with a lower risk of a new microvascular or macrovascular diabetes complication (adjusted hazard ratio = 0.96; 95% CI = 0.92-1.00; P = 0.05). CONCLUSIONS: This study design addresses limitations of previous studies and found a small but significantly lower risk of new diabetes complications associated with good adherence to oral antidiabetic medications.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".