Evidence first, practice second in arthroscopic surgery: use of placebo surgery in randomised controlled trial
Bibliographic record
Abstract
The application of evidence-based medicine helps clinicians avoid unnecessary procedures and decreases unnecessary harm for future patients while sparing economic burdens. Randomised controlled trials (RCTs) most accurately produce best research evidence. In arthroscopic surgery, however, many procedures have been extensively used without supportive evidence verified with RCTs. In this paper, we introduce two procedures (arthroscopic partial menisectomy for degenerative knees and arthroscopic subacromial decompression for subacromial pain syndrome), where over 30 years of procedure usage has continued prior to garnering evidence for the inefficacy of the procedures. The situations are attributed to the fact that clinical trials in arthroscopic surgeries are challenging given the use of placebo controls. A placebo-control RCT can accurately answer research questions about efficacy and safety of surgical procedures; however, the majority of arthroscopic surgeries in practice have not been rigorously tested against placebo surgeries. This is because preparing surgical placebo controls, known as sham surgeries, are ethically controversial. Also considering that high-quality study results often do not change clinical practice due to insufficient knowledge translation, the benefits of such trials may be uncertain to society at large. Additionally, there are a lack of clear guidelines for conducting arthroscopic placebo surgeries in RCTs. We hope that this article helps drive discussion about appropriate use of placebo surgeries in RCTs to produce the best quality evidence in arthroscopic surgery.
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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 | 0.425 | 0.724 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".