Expertise-based design in surgical trials: a narrative review
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are the most robust study design for evaluating the safety and efficacy of a therapeutic intervention. However, their internal validity are at risk when evaluating surgical interventions. This review summarizes existing expertise- based trials in surgery and related methodological concepts to guide surgeons performing this work. We provide caseloads required to reach the learning curve for various surgical interventions and report criteria for expertise from published and unpublished expertise-based trials. In addition, we review design and implementation concepts of expertise-based trials, including recruitment of surgeons, crossover, ethics, generalizability, sample size and definitions for learning curve. Several RCTs have used an expertise-based design. We found that the majority of definitions used for expertise were vague, heterogeneous, and inconsistent across trials evaluating the same surgical intervention. Statistical methods exist to adjust for the learning curve; however, there is limited guidance. We developed the following criteria for surgical expertise for future trials: 1) decide on the proxy to be used for the learning curve, and 2) assess eligible surgeons by comparing their performance to the previously defined expertise criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.095 | 0.393 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".