Need for expertise based randomised controlled trials
Bibliographic record
Abstract
Although conventional randomised controlled trials are widely recognised as the most reliable method to evaluate pharmacological interventions,1 2 scepticism about their role in nonpharmacological interventions (such as surgery) remains.3-6 Conventional randomised controlled trials typically randomise participants to one of two intervenions (A or B) and individual clinicians give intervention A to some participants and B to others. An alternative trial design, the expertise based randomised controlled trial, randomises participants to clinicians with expertise in intervention A or clinicians with expertise in intervention B, and the clinicians perform only the procedure they are expert in. We present evidence to support our argument that increased use of the expertise based design will enhance the validity, applicability, feasibility, and ethical integrity of randomised controlled trials in surgery, as well as in other areas. We focus on established surgical interventions rather than new surgical procedures in which clinicians have not established expertise.
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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 | 0.563 | 0.791 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.034 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.031 | 0.024 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".