Mechanisms of Neuromuscular Fatigability in People with Cancer-Related Fatigue
Bibliographic record
Abstract
INTRODUCTION: Cancer-related fatigue (CRF) is a debilitating symptom that affects around one-third of people for months or years after cancer treatment. In a recent study, we found that people with posttreatment CRF have greater neuromuscular fatigability. The aim of this secondary analysis was to examine the etiology of neuromuscular fatigability in people with posttreatment CRF. METHODS: Ninety-six people who had completed cancer treatment were dichotomized into two groups (fatigued and nonfatigued) based on a clinical cut point for fatigue. Alterations in neuromuscular function (maximal voluntary contraction peak force, voluntary activation, potentiated twitch force, and EMG) in the knee extensors were assessed across three common stages of an incremental cycling test. Power outputs during the fatigability test were expressed relative to gas exchange thresholds to assess relative exercise intensity. RESULTS: The fatigued group had a more pronounced reduction in maximal voluntary contraction peak force and potentiated twitch force throughout the common stages of the incremental cycling test (main effect of group: P < 0.001, ηp2 = 0.18 and P = 0.029, ηp2 = 0.06, respectively). EMG was higher during cycling in the fatigued group (main effect of group: P = 0.022, ηp2 = 0.07). Although the relative intensity of cycling was higher in the fatigued group at the final common stage of cycling, this was not the case during the initial two stages, despite the greater impairments in neuromuscular function. CONCLUSIONS: Our results suggest that the rapid impairments in neuromuscular fatigability in people with CRF were primarily due to disturbances at the level of the muscle rather than the central nervous system. This could affect the ability to tolerate daily physical activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".