Prediction of antipsychotic medication inception in antipsychotic-naive youth at clinical high risk for psychosis
Bibliographic record
Abstract
Abstract Background Antipsychotic (AP) medication in individuals at clinical high risk for psychosis (CHR-P) is not routinely recommended by clinical guidelines but is commonly prescribed. Since little is known about the predictors of AP inception in CHR-P, we analyzed data from two observational cohorts. Methods To avoid baseline predictors being confounded by previous treatment, participants were selected for analysis from the 764 participants at CHR-P enrolled in NAPLS-2 and the 710 enrolled in NAPLS-3 by excluding those with lifetime histories of AP use. Baseline clinical variables available in both studies were employed as predictors of subsequent AP inception over the next 6 months in univariable and multivariable analyses. Results Preliminary analyses indicated no important effects of sample. The final combined population included 79 AP inception participants and 580 participants who did not have AP inception. The AP medications most commonly prescribed were risperidone, aripiprazole, and quetiapine. Univariable analyses identified seven significant predictors of AP inception. The final logistic regression model including these variables was highly significant (χ 2 = 36.53, df = 7, p = <0.001). Three variables (current major depression , fewer education years, and current benzodiazepine use) emerged as significant independent predictors of AP inception. Conclusion This study is the first to determine baseline characteristics that predict subsequent AP initiation in CHR-P. Some AP use in CHR-P appears to be intended as augmentation of antidepressant treatment for comorbid major depression. Some prescribers may not have detected the attenuated positive symptoms characteristic of CHR-P since their severity did not significantly predict AP inception.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".