Placebo effects in adult and adolescent patients with schizophrenia: combined analysis of nine <scp>RCT</scp>s
Bibliographic record
Abstract
OBJECTIVE: To examine characteristics of placebo responders and seek optimal criteria of early improvement with placebo for predicting subsequent placebo response in patients with schizophrenia. METHOD: Data of 672 patients with schizophrenia randomized to placebo in nine double-blind antipsychotic trials were analyzed. Multiple logistic regression analyses were conducted to examine associations between placebo response at week 6 (i.e., a ≥ 25% reduction in the Positive and Negative Syndrome Scale [PANSS] score) and gender, age, study locations, baseline PANSS total or Marder 5-Factor scores, and per cent PANSS score reduction at week 1. Predictive power of improvement at week 1 for subsequent response was investigated; sensitivity and specificity of incremental 5% cutoff points between 5% and 25% reduction in the PANSS total score at week 1 were calculated. RESULTS: Per cent PANSS total score reduction at week 1 and lower PANSS Marder disorganized thought scores at baseline were significantly associated with subsequent placebo response. A 10% reduction in a per-protocol analysis or a 15% reduction in last-observation-carried-forward analysis in the PANSS total score at week 1 showed the highest predictive power. CONCLUSION: These findings are informative to identify potential placebo responders at the earliest opportunity for optimal trial design for schizophrenia.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".