Can Patients at Risk for Persistent Negative Symptoms Be Identified During Their First Episode of Psychosis?
Bibliographic record
Abstract
Patients with schizophrenia who show persistent negative symptoms are an important subgroup, but they are difficult to identify early in the course of illness. The objective of this study was to examine characteristics that discriminate between first-episode psychosis (FEP) patients in whom primary negative symptoms did or did not persist after 1 year of treatment. Patients with a DSM-IV diagnosis of FEP whose primary negative symptoms did (N = 36) or did not (N = 35) persist at 1 year were contrasted on their baseline and 1-year characteristics. Results showed that patients with persistent primary negative symptoms (N = 36) had a significantly longer duration of untreated psychosis (p < .005), worse premorbid adjustment during early (p < .001) and late adolescence (p < .01), and a higher level of affective flattening (p < .01) at initial presentation compared with patients with transitory primary negative symptoms. The former group also showed significantly lower remission rates at 1 year (p < .001). Multiple regression analysis confirmed the independent contribution of duration of untreated psychosis, premorbid adjustment, and affective flattening at baseline to the patients' likelihood of developing persistent negative symptoms. It may therefore be possible to distinguish a subgroup of FEP patients whose primary negative symptoms are likely to persist on the basis of characteristics shown at initial presentation for treatment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".