Participating in Multicenter Randomized Controlled Trials: What’s the Relative Value?
Bibliographic record
Abstract
The value of high-quality, large-scale, multicenter randomized controlled trials (RCTs) in orthopaedic surgery is becoming well recognized; however, the efforts of investigators participating in RCTs are often underappreciated in areas such as academic merit. Within this manuscript, we discuss how involvement in a large-scale RCT can lead to benefits, such as improvements to clinical practice and decision-making as well as personal incentives. We also examined how investigators' contributions to large multicenter RCTs are perceived and recognized by academic promotion committees. We found that academic promotion committees undervalue contributions to multicenter RCTs as compared with participation in studies that offer lower levels of evidence. The culture of academic promotion needs to evolve to ensure that participation in large multicenter RCTs is appropriately valued by these committees.
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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 | 0.656 | 0.885 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".