Gesture Performance in First- and Multiple-Episode Patients with Schizophrenia Spectrum Disorders
Bibliographic record
Abstract
BACKGROUND/AIM: Gesturing plays an important role in social behavior and social learning. Deficits are frequent in schizophrenia and may contribute to impaired social functioning. Information about deficits during the course of the disease and presence of severity and patterns of impairment in first-episode patients is missing. Hence, we aimed to investigate gesturing in first- compared to multiple-episode schizophrenia patients and healthy controls. METHODS: In 14 first-episode patients, 14 multiple-episode patients and 16 healthy controls matched for age, gender and education, gesturing was assessed by the comprehensive Test of Upper Limb Apraxia. Performance in two domains of gesturing - imitation and pantomime - was recorded on video. Raters of gesture performance were blinded. RESULTS: Patients with multiple episodes had severe gestural deficits. For almost all gesture categories, performance was worse in multiple- than in first-episode patients. First-episode patients demonstrated subtle deficits with a comparable pattern. CONCLUSIONS: Subjects with multiple psychotic episodes have severe deficits in gesturing, while only mild impairments were found in first-episode patients independent of age, gender, education and negative symptoms. The results indicate that gesturing is impaired at the onset of disease and likely to further deteriorate during its course.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".