The impact of exercise interventions on the network structure of psychotic symptoms: analysis from two clinical trials
Bibliographic record
Abstract
Background and hypothesis In people with psychotic disorders, exercise interventions are known to improve psychotic symptoms, however, the mechanisms underlying these effects are unclear. In the network approach, mental disorders are conceptualized as complex systems of interacting symptoms. In this context, exercise interventions could modify the dynamic of psychotic symptoms within the network. Using data from two independent clinical trials using exercise, the aim was to investigate the impact of exercise interventions on network connectivity, then compare the network structure pre and post intervention. Study designCombined data from two clinical trials on exercise with a total of 106 participants with a diagnostic of psychotic disorder were included. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptoms severity using semi-structured interviews. Networks before and after PE were performed. Study results At baseline, the PANSS network was densely connected with several strong positive con-nections between the symptoms with negative symptoms being the most central. After exercise, the network was less dense and less connected, and the connections were dif-ferent. When the networks before and after exercise were compared, they were signifi-cantly different in terms of structure, but not global strength. Conclusion This study is the first to show that exercise seems to favor a disconnection between psychotic symptoms, and could modify the network structure, providing a first mechanism of action which would require more investigation.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".