The neuromuscular basis of functional impairment in schizophrenia: A scoping review
Bibliographic record
Abstract
Patients with schizophrenia exhibit functional impairments in their locomotory tasks, which decreases their quality of life. Due to the limited current research, the neuromuscular mechanisms behind the functional impairments in patients is not fully understood. Thus, this review aims to summarize the neuromuscular mechanisms that underlie these deficits in daily functioning. These deficits are speculated to stem from abnormalities at various levels from neurons through to the skeletal muscles. The neurological abnormalities are exhibited as lower motor neuron dysfunction whereas the skeletal muscle pathology is shown as increased muscle fibre (type 1 and type 2) atrophy, reduction in maximal force generation, and increased strength loss per decade. Although antipsychotics effectively reduce positive symptoms, functional impairments remain unresolved. Both endurance and resistance training have shown potential benefits in alleviating deficits in daily functioning by increasing muscular strength, increasing fat-free mass, and preserving neuromuscular properties from degradation. In summary, the review elucidates various possible mechanisms for the onset of functional impairment experienced by patients with schizophrenia and highlights the potential utility of endurance and resistance training to alleviate these deficits in daily functioning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".