Parsing the components of the psychomotor syndrome in schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Catatonia, extrapyramidal signs, psychomotor slowing, and (motoric) neurological soft signs are well-known psychomotor symptoms in schizophrenia. This study aims at investigating the interrelations between these symptoms. In addition, associations between psychomotor symptoms, clinical symptoms, and cognitive functioning will be studied. METHOD: An extensive test battery containing psychomotor (Bush Francis Catatonia Rating Scale; St Hans Rating Scale; Salpêtrière Retardation Rating Scale; Neurological Evaluation Scale) and clinical (Positive and Negative Syndrome Scale; Calgary Depression Scale) rating scales as well as instrumental psychomotor tests (Line Copying Task; Finger Tapping Task) and cognitive tasks (Symbol Digit Substitution Test; Stroop Colour Word Test; Continuous Performance Test; Letter Number Sequencing) was administered to a sample of 124 patients with schizophrenia or schizoaffective disorder. RESULTS: Correlational analyses showed that two clusters emerge from our data: first, a psychomotor poverty cluster referring to the interrelations between catatonia, parkinsonism, psychomotor slowing, and negative symptoms; second, a cluster containing motoric neurological soft signs, which were found to be correlated with cognitive functioning. CONCLUSION: Psychomotor abnormalities are highly prevalent phenomena in schizophrenia that have to be considered as a heterogeneous construct. However, longitudinal and neurobiological research is needed to further explore the precise nature of the interrelations found in this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".