Agreement between patients' self‐report and physicians' prescriptions on cardiovascular drug exposure: the PGRx database experience
Bibliographic record
Abstract
PURPOSE: Patients' self-reported drug exposure is subjected to memory errors and different sources of bias. Utilization of prescription records is impaired with non-compliance and over-the-counter (OTC) drug utilization. This study compared patients' self-report (PS) to physician's prescriptions of cardiovascular drugs (CVDs). METHODS: The PGRx database is constituted by networks of specialized centers that recruited cases of 15 different diseases including myocardial infarction (MI) cases, and a network of general practitioners recruiting a pool of potential referents. For MI cases and referents, data on all drug utilization within the 2 years preceding the index date were obtained from PS and from physician's report of their prescriptions (PP). Patients' reports were obtained using a structured telephone interview complemented with an interview guide containing names of diseases and pictures of drug packages. Comparisons were made on exposure to each class of CVDs, for different time-windows, 2 months, 3-12 months and 13-24 months prior to the index date. RESULTS: The concordance between physician and patient report was assessed on 2702 patient-physician pairs. Agreement was excellent overall (kappa = 0.83, 95% confidence interval (CI): 0.81-0.85). Prevalences of exposure were very close between PS and PP for all classes of prescription CVDs. CONCLUSION: Using a standardized and systematic collection of information on drug exposure directly from patients appeared to provide similar information to using physician prescription records over a 2-year recall period.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".