Practical tips for surgical research: blinding: who, what, when, why, how?
Bibliographic record
Abstract
Blinding refers to the concealment of group allocation from one or more individuals involved in a clinical research study, most commonly a randomized controlled trial (RCT). Although randomization minimizes differences between treatment groups at the outset of the trial, it does nothing to prevent differential treatment of the groups later in the trial or the differential assessment of outcomes, either of which may result in biased estimates of treatment effects. The optimal strategy to minimize the likelihood of differential treatment or assessments of outcomes is to blind as many individuals as possible in a trial. Randomized controlled trials of surgical interventions are frequently more difficult to blind than RCTs of medications, which typically achieve blinding with placebos. However, imaginative techniques may make blinding more feasible in surgical trials than is commonly believed by many researchers. In this article we discuss the importance of blinding and provide practical suggestions to researchers who wish to incorporate blinding into their surgical studies.
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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 | 0.385 | 0.615 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.012 | 0.043 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.027 | 0.045 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".