Accuracy of Self-reported Prescribed Analgesic Medication Use
Bibliographic record
Abstract
OBJECTIVES: The validity of studies conducted with patient registries depends on the accuracy of the self-reported clinical data. As of now, studies about the validity of self-reported use of analgesics among chronic pain (CP) populations are scarce. The objective of this study was to assess the accuracy of self-reported prescribed analgesic medication use. This was attained by comparing the data collected in the Quebec Pain Registry (QPR) database to those contained in the Quebec administrative prescription claims database (Régie de l'assurance maladie du Québec [RAMQ]). METHODS: To achieve the linkage between the QPR and the RAMQ databases, the first 1285 patients who were consecutively enrolled in the QPR between October 31, 2008 and January 27, 2010 were contacted by mail and invited to participate in a study in which they had to provide their unique RAMQ health insurance number. Using RAMQ prescription claims as the reference standard, κ coefficients, sensitivity, specificity, and their respective 95% confidence intervals were calculated for each therapeutic class of prescribed analgesic drugs that the participants reported taking currently and in the past 12 months. RESULTS: A total of 569 QPR patients responded to the postal mailing, provided their unique health insurance number, and gave informed consent for the linkage (response proportion=44%). Complete RAMQ prescription claims over the 12 months before patient enrollment into the QPR were available for 272 patients, who constituted our validated study population. Regarding current self-reported prescribed analgesic use, κ coefficients measuring agreement between the 2 sources of information ranged from 0.66 to 0.78 for COX-2-selective nonsteroidal anti-inflammatory drugs, anticonvulsants, antidepressants, skeletal muscle relaxants, synthetic cannabinoids, opiate agonists/partial agonists/antagonists, and antimigraine agents therapeutic classes. For the past 12-month self-reported prescribed analgesic use, QPR patients were less accurate regarding anticonvulsants (κ=0.59), opiate agonists/partial agonists/antagonists (κ=0.57), and antimigraine agents use (κ=0.39). DISCUSSION: Information about current prescribed analgesic medication use as reported by CP patients was accurate for the main therapeutic drug classes used in CP management. Accuracy of the past year self-reported prescribed analgesic use was somewhat lower but only for certain classes of medication, the concordance being good on all the others.
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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 | 0.005 | 0.018 |
| 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 | 0.000 |
| 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".