Predictors of Long-term Persistence on Statins in a Subsidized Clinical Population
Bibliographic record
Abstract
OBJECTIVES: The use of statins in primary prevention of cardiovascular disease is currently under debate. This study characterizes and identifies predictors of the persistence of use of statins in a clinical cohort of subsidized new users of similar age to the WOSCOPS trial subjects. METHODS: Medical, pharmaceutical, and demographic records for the period January 1, 1987 through December 31, 1994 were extracted from the databases of Québec's provincial health plan for a 10% random sample of social assistance recipients. Patients remained eligible for inclusion if they had received a first dispensation of a statin between January 1, 1987 and July 31, 1994. Persistence was defined as the number of days on treatment with a statin while continuing to renew dispensations within a defined time limit. RESULTS: New users of statins included 983 social assistance recipients who were observed for a total of 2,439,153 person-days. Median persistence on statin treatment was 173 (95% CI = 155, 204) days. Only 13% of patients persisted for 5 years of treatment. A higher index of chronic morbidity, pre-existing cardiovascular disease, and previous use of nicotinic acid were predictive of longer persistence on statin medication. Those patients whose first statin dispensation was for lovastatin discontinued treatment earlier than those whose first dispensation was for pravastatin or simvastatin. CONCLUSIONS: New users showed low persistence on statins in a cohort of socially assisted persons aged 45-64, in spite of the minimal financial cost of the drug for such beneficiaries of Québec's provincial health plan.
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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 | 0.000 |
| 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) | 0.001 | 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".