Neuromuscular function and fatigability in people diagnosed with head and neck cancer before versus after treatment
Bibliographic record
Abstract
Purpose: Treatment for head and neck cancer is associated with multiple side-effects, including loss of body mass, impaired physical function, and reduced health-related quality of life. This study aimed to investigate the impact of treatment (radiation therapy ± concurrent chemotherapy) on (i) muscle strength, muscle cross-sectional area and patient-reported outcomes, and (ii) central and peripheral alterations during a whole-body exercise task.Methods: Ten people with head and neck cancer (4 female; 50±9 years) completed a lab visit before and after (56±30 days) completion of treatment. Participants performed a neuromuscular assessment (involving maximal isometric voluntary contractions in the knee extensors and electrical stimulation of the femoral nerve) before and during intermittent cycling to volitional exhaustion. Anthropometrics and patient-reported outcomes were also assessed.Results: From before to after treatment, maximal isometric muscle strength was reduced (P=0.002, d=0.73), as was potentiated twitch force (P<0.001, d=0.62), and muscle cross-sectional area (e.g. vastus lateralis: P=0.010, d=0.64). Exercise time was reduced (P = 0.008, d = 0.62) and peripheral processes contributed to a reduction in maximal force due to cycling. After treatment, the severity of self-reported fatigue increased (P=0.041, r=-0.65) and health-related quality of life decreased (P=0.012, r=-0.79).Conclusion: Neuromuscular function was impaired in patients with head and neck cancer after treatment. Whole-body exercise tolerance was reduced and resulted in predominantly peripheral, rather than central, disturbances to the neuromuscular system. Future research should evaluate strength training after treatment for head and neck cancer, with the overall aim of reducing fatigue and improving health-related quality of life.
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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 | 0.002 |
| 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) | 0.001 | 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".