Does early antipsychotic response predict long‐term treatment outcome?
Bibliographic record
Abstract
OBJECTIVE: Early antipsychotic response within the first 2-3 weeks of treatment can predict short-term outcomes after several months. We conducted the current study to determine whether the predictive value of early antipsychotic response persists throughout long-term treatment over multiple years. METHODS: In this observational study, we conducted follow-up assessments of 64 patients with first-episode psychosis an average of 25 months after they began antipsychotic treatment. Patients were initially randomized to receive haloperidol or olanzapine, but treatment after the acute hospitalization period was not controlled. Regression analyses were used to determine whether early improvement on the Brief Psychiatric Rating Scale at 2 or 3 weeks predicted longer term improvement at follow-up. We conducted secondary analyses to determine whether early response could predict extrapyramidal side effects at follow-up. RESULTS: Early response to haloperidol at 2 weeks predicted Brief Psychiatric Rating Scale improvement on longer term follow-up (p = .002). Longer term improvement was not predicted by early response to olanzapine at 2 weeks (p = .726) or 3 weeks (p = .541). Rates of extrapyramidal side effects did not differ between treatment groups and were not predicted by early response. CONCLUSION: These results demonstrate the long-term prognostic value of early haloperidol response. The predictive value of early olanzapine response may be less robust.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".