T116. PREDICTION OF REMISSION IN NON-CONVERTING INDIVIDUALS AT CLINICAL HIGH RISK FOR PSYCHOSIS
Bibliographic record
Abstract
Abstract Background The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied in the past 30 years with the goal of understanding the development of psychosis. Despite the progress in understanding what factors are associated with conversion to psychosis from the CHR-P state, less attention has been paid to the individuals who do not transition to psychosis. It is estimated that approximately 75–80% of individuals do not go on to convert to psychosis from the CHR-P state and this group should not simply be characterized as the inverse of conversion. To date, only a handful of studies have examined the characteristics and predictors of those who do not convert to psychosis and ultimately either remit or continue to meet symptom-based CHR-P criteria. The present study took an exploratory empirical approach to determining potential factors that predict remission in non-converters. Methods Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS2). Univariate Kaplan Meier survival analyses were performed on a pool of available demographic and clinical variables. Variables that were significant (p < 0.05) in the univariate analyses were then included in a multivariate Cox proportional hazard regression to predict remission. Remission was defined as all SOPS positive symptom subscale items rated as a 2 or lower at any given follow-up visit. Results A total of 359 participants from the NAPLS2 study who did not convert to psychosis and had data for at least the baseline and first follow-up visit and were included in this study. Of these participants, 174 met criteria for symptomatic remission. A total of 57 clinical variables were tested in univariate analyses and 14 of these variables met criteria for inclusion in the multivariate model. The variables included in the multivariate model were demographic variables (ethnicity, stressful life events), items from the Scale of Prodromal Symptoms (SOPS) (avolition, dysphoric mood), subtest scores from the MATRICS Cognitive Battery (speed of processing, verbal learning, verbal and non-verbal working memory, reasoning and problem solving, visual learning), one item from the Calgary Depression Scale for Schizophrenia (CDSS) (pathological guilt) and measures of functioning (GAF decline in past year, lowest GAF score in the past year). Overall, the multivariate model achieved a C-index of 0.64 (SE = 0.02) and p-value of 0.001 in predicting remission. In the multivariate model, significant covariates included stressful life events (HR = .95, p = .006), Hispanic ethnicity (HR = 1.45, p = .045), and avolition (HR = .89, p = .04). Covariates approaching significance included visual learning (HR = 1.02, p = .07), and GAF decline in the past year (HR = 1.01, p = .09). Discussion This study is the first to use a data-driven approach to systematically assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The identified set of significant clinical variables is novel, suggesting that remission represents a unique clinical phenomenon and suggesting that further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P period.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".