Empirical Assessment of the Factorial Structure of Clinical Symptoms in Schizophrenia
Bibliographic record
Abstract
The Positive and Negative Syndrome Scale (PANSS) is widely used as a method for the assessment of symptoms of schizophrenia but the most complete model of how symptoms are structured has not been determined. Using the methods of confirmatory factor analysis with a large sample of 1,233 of schizophrenic subjects this study examined the goodness of fit of 20 previously proposed models. None of these proposed models met criteria for adequate fit to the empirical data. The sample was then stratified and half of the data was used to calibrate a new model. The model was validated in the second half of the data. The new pentagonal model uses 25 of the 30 items of the PANSS in 5 factors: positive, negative, dysphoric mood, activation, and and autistic preoccupation. Patients who varied widely in age, severity, and chronicity of illness did not differ in their overall symptom structure. The results of this study also implicated some problems in the validity of the PANSS as currently configured when used to assess symptoms of schizophrenia.
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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 | 0.027 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".