Assessment of patient life engagement in schizophrenia using items from the Positive and Negative Syndrome Scale
Bibliographic record
Abstract
BACKGROUND: Improved patient life engagement is a meaningful treatment goal in schizophrenia that cannot be satisfactorily measured using existing tools. This research aimed to determine whether certain items from the Positive and Negative Syndrome Scale (PANSS) can assess patient life engagement in schizophrenia. METHODS: Three approaches were used to identify PANSS items that reflect patient life engagement: (1) a panel discussion with expert psychiatrists (n = 4); (2) interviews with patients with schizophrenia (n = 20); and (3) a principal component analysis to explore clustering of items (n = 954 from three randomized controlled trials). Internal consistency was assessed by Cronbach's alpha and item-total correlations. A minimal clinically important difference (MCID) was determined by anchor- and distribution-based methods. RESULTS: Expert psychiatrists identified 11 relevant items, and patients rated 13 items as "very relevant" to patient life engagement, most of which clustered in the principal component analysis. Considering all results, a composite set of 14 PANSS items that may be relevant to patient life engagement in schizophrenia was devised: P2, N1, N2, N3, N4, N5, N6, N7, G6, G7, G11, G13, G15, G16 (Cronbach's alpha, 0.84; item-total correlations, 0.35-0.56, indicating acceptable correlation with the underlying concept; exception: G6 [depression], 0.19). An MCID of 5 points (small/moderate improvement) or 10 points (large improvement) may be appropriate. CONCLUSIONS: A subset of 14 PANSS items may be used to reflect patient life engagement in clinical practice/trials in schizophrenia, complementing the results of traditional psychiatric symptom scales with a patient-centered outcome that is relevant to real-world treatment goals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".