Blinding, sham, and treatment effects in randomized controlled trials for back pain in 2000–2019: A review and meta-analytic approach
Bibliographic record
Abstract
Background: Blinding aims to minimize biases from what participants and investigators know or believe. Randomized controlled trials, despite being the gold standard to evaluate treatment effect, do not generally assess the success of blinding. We investigated the extent of blinding in back pain trials and the associations between participant guesses and treatment effects. Methods: We did a review with PubMed/OvidMedline, 2000–2019. Eligibility criteria were back pain trials with data available on treatment effect and participants’ guess of treatment. For blinding, blinding index was used as chance-corrected measure of excessive correct guess (0 for random guess). For treatment effects, within- or between-arm effect sizes were used. Analyses of investigators’ guess/blinding or by treatment modality were performed exploratorily. Results: Forty trials (3899 participants) were included. Active and sham treatment groups had mean blinding index of 0.26 (95% confidence interval: 0.12, 0.41) and 0.01 (−0.11, 0.14), respectively, meaning 26% of participants in active treatment believed they received active treatment, whereas only 1% in sham believed they received sham treatment, beyond chance, that is, random guess. A greater belief of receiving active treatment was associated with a larger within-arm effect size in both arms, and ideal blinding (namely, “random guess,” and “wishful thinking” that signifies both groups believing they received active treatment) showed smaller effect sizes, with correlation of effect size and summary blinding indexes of 0.35 ( p = 0.028) for between-arm comparison. We observed uniformly large sham treatment effects for all modalities, and larger correlation for investigator’s (un)blinding, 0.53 ( p = 0.046). Conclusion: Participants in active treatments in back pain trials guessed treatment identity more correctly, while those in sham treatments tended to display successful blinding. Excessive correct guesses (that could reflect weaker blinding and/or noticeable effects) by participants and investigators demonstrated larger effect sizes. Blinding and sham treatment effects on back pain need due consideration in individual trials and meta-analyses.
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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 | 0.128 | 0.306 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".