Medication adherence in first episode psychosis: the role of pre‐onset subthreshold symptoms
Bibliographic record
Abstract
OBJECTIVE: The experience of pre-onset subthreshold psychotic symptoms (STPS, signifying a clinical high-risk state) in first episode psychosis (FEP) predicts poorer outcomes during treatment, possibly through differential adherence to medication. We explored whether adherence differs between FEP patients with and without pre-onset STPS. METHODS: Antipsychotic medication adherence was compared in 263 STPS+ and 158 STPS- subjects in a specialized early intervention program for FEP. Data were gathered from a larger observational study conducted between 2003 and 2016. STPS status, sociodemographic, and baseline clinical variables were tested as predictors of non-adherence using univariate and multivariate logistic regressions. Time to onset of non-adherence was analyzed using Kaplan-Meier curves. The same predictors were tested as predictors of time to onset of non-adherence using Cox regression models. RESULTS: Medication non-adherence was higher in STPS+ participants (78.9% vs. 68.9%). STPS status (OR 1.709), substance use disorder (OR 1.767), and milder positive symptoms (OR 0.972) were significant baseline predictors of non-adherence. Substance use disorder (HR 1.410), milder positive symptoms (HR 0.990), and lack of contact between the clinical team and relatives (HR 1.356) were significant baseline predictors of time to non-adherence. CONCLUSION: FEP patients who experience pre-onset STPS are more likely to be non-adherent to antipsychotic medication over 2 years of intervention. FEP programs should routinely evaluate pre-onset symptomatology to deliver more personalized treatments, with emphasis on engaging both patients and family members from the beginning of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".