Testing Definitions of Symptom Remission in First-Episode Psychosis for Prediction of Functional Outcome at 2 Years
Bibliographic record
Abstract
BACKGROUND: To determine the clinical relevance of different definitions of symptom remission for prediction of functional outcome in first-episode psychosis (FEP). METHODS: One hundred forty-one individuals receiving treatment for an FEP at a specialized early intervention service had positive and negative symptoms and functional status rated every month over the first 2 years of treatment using the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, and Social and Occupational Functioning Assessment Scale. Subjects were classified according to 4 definitions of remission varying the criteria for severity (negative symptom inclusion/exclusion) and duration (3/6 mo sustained). RESULTS: Positive symptom remission was achieved by 94% and 84% of subjects for 3 and 6 months, respectively, compared with 70% and 56% for positive and negative symptom remission, respectively. Linear regression analyses showed that only definitions of remission containing both positive and negative symptoms independently predicted functional outcome. This was confirmed by receiver operating characteristic analyses where remission based on positive and negative symptoms was marginally better than positive symptoms alone (difference in area under the curve; z = 1.94, P = .052). There was little difference between a time criterion of remission of positive and negative symptoms of 3 (sensitivity = 100%, specificity = 42%) or 6 (sensitivity = 90%, specificity = 57%) months. DISCUSSION: Consistent with the consensus definition of remission in schizophrenia, severity of both positive and negative symptoms in defining remission in FEP is necessary although a 3-month criterion had equal predictive validity to the 6-month criterion.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".