Comparing Schizophrenia Patients With a Predicted High/Low Risk of Nonresponse Receiving Treatment with Ziprasidone and Haloperidol: A Randomized-Controlled Study
Bibliographic record
Abstract
INTRODUCTION: The aim of this double-blind randomized study was to evaluate the response to antipsychotic treatment in schizophrenia patients with predicted high/low risk of nonresponse identified by applying a set of well-established scales and predictors of outcome and to compare efficacy between ziprasidone and haloperidol. METHODS: One hundred twelve schizophrenia patients (ziprasidone: n=54; haloperidol: n=58) were rated weekly on the Positive and Negative Syndrome Scale for Schizophrenia (PANSS), the Global Assessment of Functioning Scale (GAF), the Social and Occupational Functioning Scale (SOFAS), the Simpson-Angus Scale (SAS), and Hillside Akathisia Scale (HAS). RESULTS: Ninety-two patients (82%) were predicted to have a high risk of nonresponse. No significant difference regarding PANSS improvement in this subsample was found comparing ziprasidone and haloperidol (p=0.563). Also, for the total patient sample, no significant difference was found regarding the course of the PANSS total score, GAF (p=0.921), and SOFAS (p=0.658) between ziprasidone and haloperidol. Haloperidol resulted in higher scores on the SAS (p=0.001) and HAS (p=0.011). DISCUSSION: An alarmingly high number of patients were at high risk of nonresponse to antipsychotic treatment.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".