Accuracy of the days’ supply and the number of refills allowed recorded in Québec prescription claims databases for inhaled corticosteroids
Bibliographic record
Abstract
OBJECTIVES AND HYPOTHESES: Adherence to inhaled corticosteroids (ICS) is a major issue in asthma. This study aimed to estimate the accuracy of the days' supply and number of refills allowed, variables recorded in Québec claims databases and used to estimate adherence, and to develop correction factors, if required. We hypothesised that the accuracy of the days' supply for ICS would be low whereas the accuracy of the number of refills allowed would be high. SETTING: 40 community pharmacies in Québec (Canada) and a medication registry. PARTICIPANTS: We collected data for 1108 ICS original prescriptions stored in the 40 pharmacies (sample 1), and we obtained a second sample of 2676 ICS prescriptions selected from reMed, a medication registry (sample 2). PRIMARY AND SECONDARY OUTCOMES: We estimated the concordance of the days' supply and number of refills between Québec claims databases and the original prescription from sample 1. We developed a correction factor for the days' supply in sample 1 and validated it in sample 2. Analyses were stratified by age: 0-11 and 12-64 years. RESULTS: In sample 1, the concordance for the days' supply was 39.6% (95% CI 37.6% to 41.6%) in those aged 0-11 years and 56% (54.9% to 57.2%) in those aged 12-64 years. The concordance increased to 59.4% (58.2% to 60.5%) in those aged 0-11 years and 74.2% (73.5% to 74.9%) in those aged 12-64 years after applying the correction factors in sample 2. The concordance for the refills allowed was 92.1% (91% to 93.1%) in those aged 0-11 years and 93.1% (92.5% to 93.7%) in those aged 12-64 years in sample 1. CONCLUSIONS: The accuracy of the days' supply was moderate among those aged 0-11 years and substantial among those aged 12-64 years after applying the correction factors. The accuracy of the number of refills was almost perfect in both groups.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".