Predictors of Persistence of Use of the Novel Antidiabetic Agent Acarbose
Bibliographic record
Abstract
BACKGROUND: A carbose is the first of a new class of antidiabetic agents, the alpha-glucosidase inhibitors. This study characterizes and identifies predictors of persistence of use of acarbose. METHODS: Medical, pharmaceutical, and demographic records were extracted for 2 cohorts of patients (social assistance recipients and seniors) from the databases of Quebec's provincial health plan. Patients were eligible for inclusion if they had received their first dispensation of acarbose between August 1, 1996, and December 31, 1997. The observation period included at least 1 year before the first dispensation and a minimum of 4 months after. RESULTS: New users of acarbose included 216 social assistance recipients and 677 seniors who were followed up for 82 914 and 270 041 person-days, respectively. Median persistence with acarbose treatment was 83 days (95% confidence interval, 75-105 days) for social assistance recipients and 105 days (95% confidence interval, 90-119 days) for seniors. In both cohorts, treatment by an endocrinologist vs another physician predicted longer treatment persistence. In the seniors cohort, additional determinants of (earlier) treatment discontinuation included a higher initial daily dose, previous treatment with insulin, and consultation with a gastroenterologist after treatment initiation. CONCLUSIONS: New users of acarbose showed low persistence in 2 cohorts of beneficiaries of Quebec's provincial health plan. Prescribing specialist was an important predictor of persistence in seniors and the socially assisted. The importance of 4 additional factors in seniors only led to hypotheses concerning population differences in treatment expectations and in the occurrence and tolerance of adverse effects.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".