Orthopaedic Surgeons Prefer to Participate in Expertise-based Randomized Trials
Bibliographic record
Abstract
Empiric data and theoretical arguments suggest an alternative randomized clinical trial (RCT) design, called expertise-based RCT, has enhanced validity, applicability, and ethical integrity compared with conventional RCT. Little is known, however, about whether physicians will participate in an expertise-based RCT. In a cross-sectional survey of Canadian orthopaedic surgeons, we evaluated preference for and willingness to participate in an expertise-based versus a conventional RCT if given the opportunity to participate in a trial investigating the effectiveness of high tibial osteotomy versus unicompartmental knee arthroplasty. Using an electronic survey ((c)2005 SurveyMonkey.com), we invited all 767 members of the Canadian Orthopaedic Association (2005) to participate; 276 surgeons completed the questionnaire (37.5% response rate). One hundred two surgeons (53.4%) were willing to participate in an expertise-based RCT compared with 35 surgeons (18.3%) willing to participate in a conventional RCT. Ninety-seven surgeons (52.4%) strongly or moderately preferred the expertise-based design compared with 25 (13.5%) who preferred the conventional design. For the clinical example we presented, the majority of Canadian orthopaedic surgeons were willing to participate in and preferred the expertise-based design. The expertise-based randomized clinical trial design may overcome some of the barriers to conducting clinical trials in orthopaedic surgery and improve the validity of their conclusions.
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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 | 0.475 | 0.596 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".