A randomized trial of opinion leader endorsement in a survey of orthopaedic surgeons: effect on primary response rates
Bibliographic record
Abstract
BACKGROUND: Opinion leaders have been shown to have significant influence on the practice of health professionals and patient outcomes. METHODS: Using focus groups, key informants, and sampling to redundancy techniques, we developed a questionnaire of surgeons' preferences in the treatment of tibial shaft fractures. Twenty-two well-respected and widely known orthopaedic traumatologists endorsed the questionnaire. We randomized 395 surgeon members of the Orthopaedic Trauma Association to receive either a questionnaire that included a letter informing them of the opinion leaders' endorsement, or a questionnaire without the endorsement. RESULTS: Surgeons who received the letter of endorsement had a significantly lower response rate at 2, 4, and 8 weeks. The absolute difference in response rates was 7.8% (4.6% versus 12.4%, P < 0.05) at 2 weeks, 13.1% at 4 weeks (28.6% versus 41.7% P < 0.02), and 12.3% at 8 weeks (47.5% versus 59.8% P = 0.02). CONCLUSIONS: The addition of a letter listing expert surgeons who endorse the survey lead to significantly lower primary response rates. Those interested in influencing physician responses cannot always assume a positive effect from endorsement by opinion leaders
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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 | 0.034 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".