Recency and intensification of positive symptoms enhance prediction of conversion to syndromal psychosis in clinical high-risk patients
Bibliographic record
Abstract
BACKGROUND: Early detection and intervention strategies in patients at clinical high-risk (CHR) for syndromal psychosis have the potential to contain the morbidity of schizophrenia and similar conditions. However, research criteria that have relied on severity and number of positive symptoms are limited in their specificity and risk high false-positive rates. Our objective was to examine the degree to which measures of recency of onset or intensification of positive symptoms [a.k.a., new or worsening (NOW) symptoms] contribute to predictive capacity. METHODS: We recruited 109 help-seeking individuals whose symptoms met criteria for the Progression Subtype of the Attenuated Positive Symptom Psychosis-Risk Syndrome defined by the Structured Interview for Psychosis-Risk Syndromes and followed every three months for two years or onset of syndromal psychosis. RESULTS: Forty-one (40.6%) of 101 participants meeting CHR criteria developed a syndromal psychotic disorder [mostly (80.5%) schizophrenia] with half converting within 142 days (interquartile range: 69-410 days). Patients with more NOW symptoms were more likely to convert (converters: 3.63 ± 0.89; non-converters: 2.90 ± 1.27; p = 0.001). Patients with stable attenuated positive symptoms were less likely to convert than those with NOW symptoms. New, but not worsening, symptoms, in isolation, also predicted conversion. CONCLUSIONS: Results suggest that the severity and number of attenuated positive symptoms are less predictive of conversion to syndromal psychosis than the timing of their emergence and intensification. These findings also suggest that the earliest phase of psychotic illness involves a rapid, dynamic process, beginning before the syndromal first episode, with potentially substantial implications for CHR research and understanding the neurobiology of psychosis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".