Trends in selection and timing of first‐line pharmacotherapy in older patients with Type 2 diabetes diagnosed between 1994 and 2006
Bibliographic record
Abstract
AIMS: To characterize temporal trends in the selection and timing of first-line pharmacotherapy among older patients with Type 2 diabetes. DESIGN AND METHODS: We studied five population-based cohorts every 3 years, from 1994 to 2006. In each of those years, we identified all subjects aged 66 years or older newly diagnosed with diabetes and determined the initial glucose-lowering drug and the time between diagnosis and drug initiation. We calculated the proportion of patients prescribed each agent and estimated time from diagnosis to initiation using Kaplan-Meier survival analysis. RESULTS: We identified a total of 64 368 eligible people who initiated drug therapy during the study period. From 1994 to 2006, first-line metformin use increased from 20.1 to 79.0%. Glyburide (glibenclamide) decreased from 71.1% of all first-line therapies in 1994 to 9.8% in 2006, while first-line use of insulin or combination therapy have changed little at approximately 5% each. No other medication exceeded 2% of first-line therapies. The median time from diagnosis to initiation of pharmacotherapy increased dramatically during the study period, from 1.8 years in 1994 to 4.6 years in 2006. CONCLUSIONS: Metformin has become the most commonly used initial medication for the treatment of diabetes. Although guidelines have evolved to recommend more aggressive initiation and intensification of pharmacotherapy, our results suggest that the time from diagnosis to initiation has increased substantially.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".