Item response analysis of the Positive and Negative Syndrome Scale
Bibliographic record
Abstract
BACKGROUND: Statistical models based on item response theory were used to examine (a) the performance of individual Positive and Negative Syndrome Scale (PANSS) items and their options, (b) the effectiveness of various subscales to discriminate among individual differences in symptom severity, and (c) the appropriateness of cutoff scores recently recommended by Andreasen and her colleagues (2005) to establish symptom remission. METHODS: Option characteristic curves were estimated using a nonparametric item response model to examine the probability of endorsing each of 7 options within each of 30 PANSS items as a function of standardized, overall symptom severity. Our data were baseline PANSS scores from 9205 patients with schizophrenia or schizoaffective disorder who were enrolled between 1995 and 2003 in either a large, naturalistic, observational study or else in 1 of 12 randomized, double-blind, clinical trials comparing olanzapine to other antipsychotic drugs. RESULTS: Our analyses show that the majority of items forming the Positive and Negative subscales of the PANSS perform very well. We also identified key areas for improvement or revision in items and options within the General Psychopathology subscale. The Positive and Negative subscale scores are not only more discriminating of individual differences in symptom severity than the General Psychopathology subscale score, but are also more efficient on average than the 30-item total score. Of the 8 items recently recommended to establish symptom remission, 1 performed markedly different from the 7 others and should either be deleted or rescored requiring that patients achieve a lower score of 2 (rather than 3) to signal remission. CONCLUSION: This first item response analysis of the PANSS supports its sound psychometric properties; most PANSS items were either very good or good at assessing overall severity of illness. These analyses did identify some items which might be further improved for measuring individual severity differences or for defining remission thresholds. Findings also suggest that the Positive and Negative subscales are more sensitive to change than the PANSS total score and, thus, may constitute a "mini PANSS" that may be more reliable, require shorter administration and training time, and possibly reduce sample sizes needed for future research.
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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 | 0.040 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.006 | 0.001 |
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".