F121. DOES RELAPSE CONTRIBUTE TO TREATMENT RESISTANCE? ANTIPSYCHOTIC RESPONSE IN FIRST- VS. SECOND-EPISODE SCHIZOPHRENIA
Bibliographic record
Abstract
The objective of this study was to compare trajectories of antipsychotic response before and after relapse following response from a first episode of schizophrenia or schizoaffective disorder. The current analysis included patients with a diagnosis of first-episode schizophrenia or schizoaffective disorder who met the following criteria: (1) referral to the First-Episode Psychosis Program between 2003 and 2013; (2) treatment with an oral second-generation antipsychotic according to a standardized treatment algorithm; (3) positive symptom remission; (4) subsequent relapse (i.e., second episode) in association with non-adherence; and (4) reintroduction of antipsychotic treatment. The following outcomes were used as an index of antipsychotic treatment response: change in the Brief Psychiatric Rating Scale (BPRS) total score and number of patients who achieved positive symptom remission, including 20% and 50% response improvement. A total of 130 patients were included in the analyses. All patients took the same antipsychotic in both episodes. Antipsychotic doses in the second episode were significantly higher than those in the first episode (P=0.03). There were significant episode-by-time interactions for all outcomes of antipsychotic treatment response over 1 year (all Ps<0.001) in favor of the first episode compared to the second episode. Results remained unchanged after adjusting for antipsychotic dose. The present findings suggest that antipsychotic treatment response is reduced or delayed in the face of relapse following effective treatment of the first episode of schizophrenia.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".