Meta-Regression Analysis of Placebo Response in Antipsychotic Trials, 1970–2010
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Large placebo response presents a major challenge for psychopharmacologic drug development and contributes to the increasing failure of psychiatric trials. The objective of this meta-regression analysis was to identify potential contributors to placebo response in randomized controlled trials of antipsychotic treatment in schizophrenia. METHOD: The authors extracted trial design and clinical variables from eligible randomized controlled trials (N=50) identified through searches of MEDLINE (1960-2010) and other sources. Standardized mean change (SMC) was used as the effect size measure for placebo response, based on change scores on the Brief Psychiatric Rating Scale or the Positive and Negative Syndrome Scale from baseline to endpoint (2 to 12 weeks). RESULTS: The results suggest significant heterogeneities (Q=387.83, df=49) in the magnitude of placebo response (mean SMC, -0.33, range -1.4 to 0.9) and in study quality. Both placebo SMC and study quality increased over time. Younger age, shorter duration of illness, greater baseline symptom severity, and shorter trial duration were significantly associated with greater placebo response, while country (United States compared with other countries) was not. More study sites, fewer university or Veterans Affairs treatment settings, and a lower percentage of patients assigned to receive placebo were associated with a greater placebo response, but these were not independent of publication year. Study quality affected the variability but not mean levels of placebo response. CONCLUSIONS: This study identified important patient characteristics and trial design factors affecting the level of placebo response and hence the likelihood of detecting efficacy signals in randomized controlled trials. Future studies should test whether controlling these factors improves the detection of an antipsychotic effect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it