The Relationship Between Early Haloperidol Response and Associated Extrapyramidal Side Effects
Bibliographic record
Abstract
BACKGROUND: Early response to antipsychotic medication within 2 weeks of initiating treatment can predict psychiatric outcomes. However, it is unclear whether early response is also predictive of extrapyramidal side effects (EPSs) associated with antipsychotic medications. METHODS: In this study, we investigated 136 consecutive antipsychotic-naive, first-episode psychosis patients naturalistically treated with haloperidol. Patients were assessed at baseline and weekly after treatment initiation using the Brief Psychiatric Rating Scale, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale. Dystonia, parkinsonism, akathisia, and dyskinesia were also assessed weekly using standardized rating scales. Regression analyses were used to determine whether early response at week 2 of treatment predicted the incidence of EPS at any point during hospitalization. A secondary analysis was conducted to determine whether early response continued to predict EPS in patients who experienced no EPS within the first 2 weeks of treatment. RESULTS: The analyses demonstrated that greater Brief Psychiatric Rating Scale percent improvement at week 2 predicted a decreased risk of EPSs (P = 0.004), even in patients who did not show any EPSs within the first 2 weeks of treatment (P = 0.005). For specific EPS, early response predicted decreased incidences of parkinsonism (P = 0.028) and dyskinesia (P = 0.025), but not akathisia or dystonia. Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale improvement at week 2 did not predict EPSs. In addition, EPSs were not predicted by the maximum antipsychotic dose received during hospitalization. CONCLUSIONS: These results indicate that early antipsychotic response is valuable not only for predicting psychiatric outcomes, but also for predicting the risk of EPSs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".