Converting scores between the PANSS and SAPS/SANS beyond the positive/negative dichotomy
Bibliographic record
Abstract
Background: Previous work provided conversion equations for overall indices of positive and negative symptomatology between the two most widely used scales to assess symptom severity in schizophrenia, namely the Positive and Negative Syndrome Scale (PANSS) and the Scales for the Assessment of Positive/Negative Symptoms (SAPS/SANS). Our objective was to provide such conversion equations for subdomains of positive and negative symptomatology in order to better account for the diversity of symptom profiles in schizophrenia.Method: Symptoms severity was assessed using both the PANSS and SAPS/SANS in 205 patients with schizophrenia. Two exploratory factor analyses combining items from both scales were first performed separately in the positive and negative symptom domains. For each identified factor, linear regression analyses were then conducted to obtain conversion equations from the PANSS to the SAPS/SANS and vice versa. Linear regression model estimation was performed on 80% of the data, and reliability was then evaluated on the 20% remaining data using intra-class correlation coefficient between the original and predicted scores. This procedure was repeated 100 times with random samplings for each factor cross-scale conversion.Results: Three-factor solutions were favored both in the positive and negative symptom domains. Based on the nature of items that strongly loaded on the different factors, positive factors were termed ‘Hallucinations’, ‘Delusions’ and ‘Disorganization’, while negative factors were associated with ‘Expressivity’, ‘Amotivation’ and ‘Cognition’. Intra-class correlation coefficients between the original and predicted scores were good to excellent (0.68-0.87) for all regressions, but for the cognition factor which were deemed as low (0.25, 0.26).Conclusion: The symptom subdomains identified by the decomposition of the positive and negative domains were consistent with current descriptions of symptom dimensions in schizophrenia. With the exception of the cognition subdomain, symptom severity scores can be converted with good accuracy between scales, beyond the positive/negative symptom dichotomy. Conversion equations are implemented in an R Shiny app to facilitate their use by the clinical research community.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".