Impact of Prescriber Nonresponse on Patient Representativeness
Bibliographic record
Abstract
BACKGROUND: In pharmacoepidemiology studies where patients are selected by prescribers, there is concern that the patients of responding prescribers are not necessarily an unbiased sample of all patients. However, this usually cannot be explored. In the CADEUS study, patients and prescribers were independently contacted so that data are available for patients irrespective of whether their prescriber responded or not. Our objective was to compare the characteristics of patients whose prescriber did or did not respond. METHODS: The CADEUS study included patients treated with COX-2 inhibitors (celecoxib, rofecoxib) or traditional NSAIDs from September 2003 to August 2004. Redeemed prescriptions were randomly sampled on a monthly basis within the database of the French national healthcare insurance system for salaried persons during 1 year. Patients and prescribers were questioned independently. Data from patients and from the database were used to compare patients whose prescriber responded and those whose prescriber did not. RESULTS: Of 45,217 patients, 26,618 had prescriber data. Patients whose prescriber responded were similar to patients whose prescriber did not respond for the main study outcomes: age (56.8 +/- 16.3 years vs. 56.1 +/- 16.3 years), sex (66.0% female vs. 64.8%), cardiovascular disease history (52.2% vs. 52.0%), gastrointestinal disease history (39.5% vs. 39.4%), concomitant prescription of gastroprotective agents (22.4% vs. 23.7%), and NSAID indication, prescription type, use, and duration. CONCLUSIONS: We found no evidence for a difference between patients whose prescriber responded and patients whose prescriber did not participate in the study.
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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 | 0.201 | 0.469 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".