Persistence Patterns with Oral Antidiabetes Drug Treatment in Newly Treated Patients—A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: We assessed persistence patterns with oral antidiabetes drug (OAD) in patients newly dispensed with different OAD classes. METHODS: We conducted a population-based cohort study using Quebec Health Insurance Board data. Patients aged 18 years or more newly dispensed an OAD between January 1, 1998 and December 31, 2003 were included in the study (n=98,940). Persistence was defined as consistently refilling a prescription for the initial OAD within three times the days' supply of the preceding claim. For nonpersistent patients, a second course of therapy was defined as treatment initiation with any OAD after a first discontinuation. Patients were followed from treatment initiation up to December 31, 2004, ineligibility for the drug plan or death, whichever came first, and treatment discontinuation or second course of treatment. Cox regression models were used to compute adjusted hazards ratios (AHR) of persistence and initiation of second courses of therapy. RESULTS: The probability of persisting with the initial OAD over a 12-month period was 65% and 56% for patients initiated on metformin and sylfonylurea, respectively. Compared to metformin, the likelihood of discontinuing the initial OAD over the study period was significantly higher for patients on sulphonylureas (AHR: 1.32; 95% CI 1.29-1.34). Patients started on sulphonylureas were also less likely to start a second course of therapy after a first treatment discontinuation (AHR: 0.91; 95% CI 0.89-0.93). CONCLUSIONS: Compared to diabetic patients initiated on metformin, those initiated on sulphonylureas displayed poorer persistence patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".