A survey of physician efficacy requirements to plan clinical trials
Bibliographic record
Abstract
PURPOSE: Eliciting physician efficacy requirements for utilizing medical treatments can be a useful means of helping plan a clinical trial. Efficacy requirements were studied for female stress urinary incontinence, where an experimental treatment (collagen injection) was compared to the standard therapy (surgery). METHODS: A self-administered questionnaire was sent to 223 North American urologists, gynecologists, and urogynecologists. An interviewer also administered a similar questionnaire to 20 other clinician-specialists. RESULTS: The response rate for the self-administered questionnaire was 48.4% (108/223). All 20 clinician-specialists who were approached for an interview consented. On average, respondents to the self-administered questionnaire indicated they would consider using collagen as the first line treatment if the absolute reduction in efficacy of collagen versus surgery was no larger than 23%. The corresponding result for the interview-questionnaire was 22%. Efficacy was measured as patient satisfaction with treatment. In the opinion of the physicians, surgery would remain the standard therapy if the reduction was greater than 34% (self-administered questionnaire), or 37% (interviewer-administered questionnaire). CONCLUSIONS: The elicitation of physician efficacy requirements provides an idea of the treatment effect that would be needed for a clinical trial to have an impact on medical practice. These requirements can be used to calculate a relevant sample size.
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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 | 0.187 | 0.415 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".