Neurological Soft Signs in Schizophrenia: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Neurological soft signs (NSS) are hypothesized as candidate endophenotypes for schizophrenia, but their prevalence and relations with clinical and demographic data are unknown. The authors undertook a quantification (meta-analysis) of the published literature on NSS in patients with schizophrenia and healthy controls. A systematic search was conducted for published articles reporting NSS and related data using standard measures in schizophrenia and healthy comparison groups. METHOD: A systematic search was conducted for published articles reporting data on the prevalence of NSS in schizophrenia using standard clinical rating scales and healthy comparison groups. Meta-analyses were performed using the Comprehensive Meta-analysis software package. Effect sizes (Cohen d) indexing the difference between schizophrenic patients and the healthy controls were calculated on the basis of reported statistics. Potential moderator variables evaluated included age of patient samples, level of education, sample sex proportions, medication doses, and negative and positive symptoms. RESULTS: A total of 33 articles met inclusion criteria for the meta-analysis. A large and reliable group difference (Cohen d) indicated that, on average, a majority of patients (73%) perform outside the range of healthy subjects on aggregate NSS measures. Cognitive performance and positive and negative symptoms share 2%-10% of their variance with NSS. CONCLUSIONS: NSS occur in a majority of the schizophrenia patient population and are largely distinct from symptomatic and cognitive features of the illness.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".