A quantitative review of the profile and time course of symptom change in schizophrenia treated with clozapine
Bibliographic record
Abstract
Contemporary analyses demonstrate an early response to antipsychotic treatment in non-refractory schizophrenia. The profile of response to clozapine is unknown. We used meta-analytic and statistical procedures to examine the response profile to clozapine. We identified 19 unique, randomized, double-blind controlled clinical trials with suitable time course data, representing 1745 subjects. Individual subject data were available for 419 subjects, obtained from two industry-sponsored trials. Symptom severity scores from the BPRS or the PANSS were entered into regression analyses to estimate linear and quadratic coefficients of the rate of change of symptom severity over 4 weeks. Both linear and quadratic regression coefficients for clozapine, and for comparator antipsychotics differed significantly from zero (p ≤ 0.001), indicating early response profiles. Compared with other antipsychotic arms, for clozapine the treatment response was greater (d = -0.578, p = 0.021), and the linear coefficient was steeper (d = -0.502, p = 0.042); the quadratic coefficients indicating attenuation did not differ. Analyses of 6-week data and individual subject data from non-refractory and refractory trials were consistent with the primary findings. Somewhat surprisingly, clozapine shows an early response profile, similar in pattern but somewhat larger in magnitude than other antipsychotic drugs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".