The different trajectories of antipsychotic response: antipsychotics <i>versus</i> placebo
Bibliographic record
Abstract
BACKGROUND: It is generally accepted that antipsychotics are more effective than placebo. However, it remains unclear whether antipsychotics induce a pattern or trajectory of response that is distinct from placebo. We used a data-driven technique, called growth mixture modelling (GMM), to identify the different patterns of response observed in antipsychotic trials and to determine whether drug-treated and placebo-treated subjects show similar or distinct patterns of response. METHOD: We examined data on 420 patients with schizophrenia treated for 6 weeks in two double-blind placebo-controlled trials using haloperidol and olanzapine. We used GMM to identify the optimal number of response trajectories; to compare the trajectories in drug-treated versus placebo-treated patients; and to determine whether the trajectories for the different dimensions (positive versus negative symptoms) were identical or different. RESULTS: Positive symptoms were found to respond along four distinct trajectories, with the two most common trajectories ('Partial responder' and 'Responder') accounting for 70% of the patients and seen proportionally in both drug- and placebo-treated. The most striking drug-placebo difference was in the 'Dramatic responders', seen only among the drug-treated. The response of negative symptoms was more modest and did not show such distinct trajectories. CONCLUSIONS: Trajectory models of response, rather than the simple responder/non-responder dichotomy, provide a better statistical account of how antipsychotics work. The 'Dramatic responders' (those showing >70% response) were seen only among the drug-treated and make a significant contribution to the overall drug-placebo difference. Identifying and studying this subset may provide specific insight into antipsychotic action.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".