Blinding integrity in randomized sham-controlled trials of repetitive transcranial magnetic stimulation for major depression: a systematic review and meta-analysis
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a safe and effective treatment for major depression (MD). However, the perceived lack of a suitable sham rTMS condition might have compromised the success of blinding procedures in clinical trials. Thus, we conducted a systematic review and meta-analysis of randomized, double-blind and sham-controlled trials (RCTs) on high frequency (HF-), low frequency (LF-) and bilateral rTMS for MD. We searched the literature from January 1995 to July 2012 using Medline, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials and Scopus. The main outcome measure was participants' ability to correctly guess their treatment allocation at study end. We used a random-effects model and risk difference (RD). Overall, data were obtained from seven and two RCTs on HF- and bilateral rTMS, respectively. No RCT on LF-rTMS reporting on blinding success was found. HF- and bilateral rTMS trials enrolled 396 and 93 depressed subjects and offered an average of approximately 13 sessions. At study end, 52 and 59% of subjects receiving HF-rTMS and sham rTMS were able to correctly guess their treatment allocation, a non-significant difference (RD = -0.04; z = -0.51; p = 0.61). Furthermore, 63.3 and 57.5% of subjects receiving bilateral and sham rTMS were able to correctly guess their treatment allocation, also a non-significant difference (RD = 0.05; z = 0.49; p = 0.62). In addition, the use of angulation and sham coil in HF-rTMS trials produced similar results. In summary, existing sham rTMS interventions appear to result in acceptable levels of blinding regarding treatment allocation.
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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 | 0.099 | 0.214 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".