Randomized controlled trials in neurosurgery
Bibliographic record
Abstract
Randomized controlled trials (RCTs) have become the standard method of evaluating new interventions (whether medical or surgical), and the best evidence used to inform the development of new practice guidelines. When we review the history of medical versus surgical trials, surgical RCTs usually face more challenges and difficulties when conducted. These challenges can be in blinding, recruiting, funding, and even in certain ethical issues. Moreover, to add to the complexity, the field of neurosurgery has its own unique challenges when it comes to conducting an RCT. This paper aims to provide a comprehensive review of the history of neurosurgical RCTs, focusing on some of the most critical challenges and obstacles that face investigators. The main domains this review will address are: (1) Trial design: equipoise, blinding, sham surgery, expertise-based trials, reporting of outcomes, and pilot trials, (2) trial implementation: funding, recruitment, and retention, and (3) trial analysis: intention-to-treat versus as-treated and learning curve effect.
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 | 0.144 | 0.398 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".