Does sex influence the relation between symptoms and neurocognitive functions in schizophrenia?
Bibliographic record
Abstract
OBJECTIVE: A secondary analysis of our data to investigate if sex influences the specificity of the relationship between each of the 3 clinical syndromes (i.e., reality distortion, disorganization and psychomotor poverty) in schizophrenia and the neurocognitive functions that are thought to represent regional brain functions. PATIENTS AND DESIGN: Fifty-seven male and 30 female patients with a DSM-III-R diagnosis of schizophrenia were rated on the Scale for Assessment of Negative Symptoms and the Scale for Assessment of Positive Symptoms to derive scores for psychomotor poverty, disorganization, and reality distortion syndromes. All subjects completed a battery of neuropsychological tests purported to assess functioning of left temporal, right temporal, left basal frontal, right basal frontal, and dorsolateral prefrontal cortex. RESULTS: Correlation coefficients between syndrome scores and neuropsychological measures showed only word fluency (left frontal functioning) to have a statistically significant association with psychomotor poverty in women (p < 0.01). This relation was specific to psychomotor poverty syndrome. No relations between neurocognitive measures and symptoms were seen in men. CONCLUSIONS: The lack of specific relations between symptom dimensions in schizophrenia may be influenced by the fact that the neuronal circuitry associated with particular symptom dimensions may differ in men and women.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".