Response trajectories to clozapine in a secondary analysis of pivotal trials support using treatment response to subtype schizophrenia.
Bibliographic record
Abstract
OBJECTIVE: Groups of nonrefractory patients with schizophrenia, taking antipsychotics other than clozapine, show distinct trajectories of treatment response over time. Whether similar patterns of response occur with clozapine-treated patients remains uncertain. METHOD: We used a cluster analysis approach for longitudinal data (k-means longitudinal) to analyze individual patient data from 2 pivotal studies of clozapine, compared with chlorpromazine. Trajectories and symptom severity were examined in a younger, less chronic, mixed-sample (study 16, n=100) and in treatment-refractory (study 30, n=257) patients. RESULTS: Early-good and delayed-partial trajectory groups were observed, with the early-good trajectory group comprised of 73/100 (73.0%) from the mixed patient study, and 147/257 (57.2%) refractory patients. In the mixed patient sample, the distribution of clozapine and chlorpromazine treatments did not differ between the early-good and delayed-partial trajectory groups; in refractory patients proportionately more clozapine treatment was present in the early-good (87/147, 59.2%), compared with the delayed-partial (35/110, 31.8%), trajectory group. In the early-good trajectory group, improvement in mean symptom severity was 63% in mixed-study patients. Clozapine resistance appeared to be present in 10/50 (20.0%) mixed-study patients, and in 35/122 (28.9%) refractory patients. CONCLUSIONS: Early-good and delayed-partial response trajectories are seen in clozapine studies. The advantage of clozapine over chlorpromazine is seen most clearly in previous refractory patients, within the early-good trajectory group. Good and partial or poor responders to clozapine may merit further investigation.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".