Differences in treatment preferences between persons who enrol and do not enrol in a clinical trial.
Bibliographic record
Abstract
OBJECTIVE: To quantitatively compare preferences for treatment between persons who enrolled in a randomized controlled trial (RCT) and those who were eligible but chose not to enrol. INTERVENTIONS: Participants' thresholds for treatment were determined using a probability trade-off technique. Pertinent health states were described. If not taking Aspirin, the probabilities of stroke, myocardial infarction (MI), and major bleeding were given. Given the risks and benefits of chronic Aspirin therapy, a systematic approach was used to determine patients' thresholds for treatment (the smallest reduction in stroke or MI risk of which patients were willing to take Aspirin). RESULTS: Of 54 participants, 42 enrolled in the RCT, and 12 did not. Compared with persons who enrolled, those who did not enrol required significantly greater increments in treatment benefit to be willing to take Aspirin. CONCLUSIONS: This study shows differences in thresholds for treatment between persons who enrolled in a clinical trial and those who chose not to. Such attitudinal differences may lead to difficulty in the interpretation of clinical trials, especially those using health-related quality-of-life measures. More studies are needed to determine whether the attitudinal differences affect the generalization of results from clinical trials.
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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 | 0.079 | 0.250 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".