Social Cognitive Markers of Short-Term Clinical Outcome in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: In psychotic disorders, impairments in cognition have been associated with both clinical and functional outcome, while deficits in social cognition have been associated with functional outcome. As an extension to a recent report on neurocognition and short-term clinical outcome in first-episode psychosis (FEP), the current study explored whether social cognitive deficits could also identify poor short-term clinical outcome among FEP patients. METHODS: We defined the social-cognition domain based on the scores from the Hinting Task and the Four Factor Tests of Social Intelligence. Data were collected in 45 FEP patients and 26 healthy controls. The patients were divided into good- and poor-outcome groups based on clinical data at six months following initiation of treatment. Social cognition was compared among 27 poor-outcome, 18 good-outcome, and 26 healthy-control participants. RESULTS: Outcome groups significantly differed in the social cognition domain (z-scores: poor outcome=-2.0 [SD=1.4]; good outcome=-1.0 [SD=1.0]; p=0.005), with both groups scoring significantly lower than the control group (p<0.003). Moreover, outcome groups differed significantly only on the Cartoon Predictions subtest (z-scores: poor outcome=-2.7 [SD=2.7]; good outcome=-0.7 [SD=1.8]; p=0.001) among the five subtests used. CONCLUSIONS: Overall, social cognition appears to be compromised in all FEP patients compared to healthy controls. More interestingly, significant differences in social cognitive impairments exist between good and poor short-term clinical outcome groups, with the largest effect found in the Cartoon Predictions subtest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".