Agreement between patients' self‐report and physicians' prescriptions on nonsteroidal anti‐inflammatory drugs and other drugs used in musculoskeletal disorders: the international Pharmacoepidemiologic General Research eXtension database.
Bibliographic record
Abstract
PURPOSE: The use of prescription records for the assessment of exposure to nonsteroidal anti-inflammatory drugs (NSAIDs) does not capture over-the-counter drug use. This study compared patients' self-reported use to physician's prescriptions for NSAIDs and other drugs used to treat musculoskeletal disorders (MSDs). METHODS: The international Pharmacoepidemiologic General Research eXtension database includes a network of general practitioners recruiting patients without reference to diagnoses or prescriptions. Data on all drug use across France within the 2 years preceding the date of inclusion (index date) were obtained from both patients' self-reports (PSRs) and physicians' prescription reports (PPRs). Patients' reports were obtained using a structured telephone interview combined with an interview guide containing a list of drugs commonly used. Comparisons were made on exposure to four categories of MSD drugs and three time windows up to 24 months before the index date. RESULTS: Agreement between physician and patient reports was assessed on 4152 patient-physician pairs. Bias- and prevalence-adjusted kappa values showed fair agreement for nonaspirin NSAIDs, moderate to fair for nonnarcotic analgesics, high for osteoarthritis and moderate to substantial for muscle relaxants. Over-the-counter drug use was associated with greater disagreement (OR = 2.21, 95%CI = 1.05-1.38). Age was not associated with disagreement. CONCLUSION: Differences between PSR and PPR in estimating the prevalence of MSD drug use varied by the type of drug and the elapsed time from the index date. The patient-assisted interview method used in this study showed better agreement with PPR compared with standard interviews, especially for long time windows and patients older than 65 years. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".