Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia
Bibliographic record
Abstract
AMA Casadei V, Jaracz J, Wójciak P, et al. Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology. 2023;18(1):11-18. doi:10.5114/nan.2023.128697. APA Casadei, V., Jaracz, J., Wójciak, P., Moczko, J., Filipiak, P., & Kucharska-Kowalczyk, K. et al. (2023). Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology, 18(1), 11-18. https://doi.org/10.5114/nan.2023.128697 Chicago Casadei, Veronica, Jan Jaracz, Paweł Wójciak, Jerzy Moczko, Przemysław Filipiak, Kamila Kucharska-Kowalczyk, and Filip Rybakowski. 2023. "Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia". Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology 18 (1): 11-18. doi:10.5114/nan.2023.128697. Harvard Casadei, V., Jaracz, J., Wójciak, P., Moczko, J., Filipiak, P., Kucharska-Kowalczyk, K., and Rybakowski, F. (2023). Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology, 18(1), pp.11-18. https://doi.org/10.5114/nan.2023.128697 MLA Casadei, Veronica et al. "Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia." Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology, vol. 18, no. 1, 2023, pp. 11-18. doi:10.5114/nan.2023.128697. Vancouver Casadei V, Jaracz J, Wójciak P, Moczko J, Filipiak P, Kucharska-Kowalczyk K et al. Physical activity and negative symptoms as predictors of metabolic syndrome in patients with schizophrenia. Neuropsychiatria i Neuropsychologia/Neuropsychiatry and Neuropsychology. 2023;18(1):11-18. doi:10.5114/nan.2023.128697.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".