Randomized Controlled Trials of Surgical Interventions
Bibliographic record
Abstract
In Brief Background and Objectives: Surgical trials pose many methodological challenges often not present in trials of medical interventions. If not properly accounted for, these challenges may introduce significant biases and threaten the validity of the results. Methods: We systematically reviewed the significance of randomized controlled trials in the evaluation of surgical interventions, discussed the methodological challenges encountered in designing and conducting randomized controlled trials of surgical treatments, and proposed possible solutions to overcome these challenges. Conclusions: Many barriers and issues of surgical trials affecting internal validity can be overcome with proper methodology, and in most cases these issues do not restrict their conduct. Researchers should consider their research question carefully and design a surgical trial that contains features appropriate for the question. In doing so, they must ensure that the trial is valid, feasible, and affordable—a difficult feat, but one well worth the challenge. This article reviews the significance of randomized controlled trials in the evaluation of surgical interventions, discusses some of the methodological challenges encountered in designing and conducting randomized controlled trials of surgical trials, and proposes possible solutions to overcome these challenges.
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 | 0.142 | 0.434 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".