Examination of the <scp>P</scp>ositive and <scp>N</scp>egative <scp>S</scp>yndrome <scp>S</scp>cale factor structure and longitudinal relationships with functioning in early psychosis
Bibliographic record
Abstract
AIM: We examined the factor structure of the Positive and Negative Syndrome Scale (PANSS) in early-episode psychosis and its relationships with functioning at baseline and follow-up. METHODS: A total of 240 consecutive admissions to an early intervention in psychosis clinic were assessed at intake to the program with the PANSS, Global Assessment of Functioning (GAF) and Social and Occupational Functioning Assessment Scale (SOFAS). Seventy individuals were reassessed at follow-up. A maximum likelihood factor analysis was conducted on baseline PANSS scores and the ability of each factor to predict baseline and follow-up GAF and SOFAS was examined. RESULTS: A five-factor model with varimax rotation was the best fit to our data and was largely congruent with factors found previously. The negative symptom factor was the best predictor of GAF and SOFAS at baseline and follow-up. CONCLUSION: Negative symptoms are the best symptomatic predictor of functioning in individuals with early psychosis and are an important treatment target to improve recovery.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".