Physiological and psychosocial correlates of cancer related fatigue
Bibliographic record
Abstract
ABSTRACT Cancer-related fatigue (CRF) is a common and distressing symptom of cancer and its treatments that may persist for years following treatment completion in approximately one-third of cancer survivors. Despite its high prevalence, little is known about the pathophysiology of CRF. Using a comprehensive group of physiological and psychosocial variables, the aim of the present study was to identify correlates of CRF in a heterogenous group of cancer survivors. Ninety-three cancer survivors (51 fatigued, 42 non-fatigued, with grouping based on validated cut-off scores derived from The Functional Assessment of Chronic Illness Therapy - Fatigue scale) completed assessments of performance fatigability (i.e. the change in maximal force-generating capacity, contractile function and capacity of the central nervous system to activate muscles caused by cycling exercise), cardiopulmonary exercise testing, venous blood samples for whole blood cell count and inflammatory markers and body composition. Participants also completed questionnaires measuring demographic, treatment-related, and psychosocial variables. The results showed that performance fatigability (decline in muscle strength during exercise), time-to-task-failure, peak oxygen uptake , tumor necrosis factor-α (TNF-α), body fat percentage and lean mass index were associated with CRF severity. Performance fatigability, , TNF-α and age explained 35% of the variance in CRF severity. Furthermore, those with clinically-relevant CRF reported more pain, more depressive symptoms, less social support, and were less physically active than non-fatigued cancer survivors. Given the association between CRF and numerous physical activity related measures, including performance fatigability, cardiorespiratory fitness, and anthropometric measures, the present study identifies potential biomarkers by which the mechanisms underpinning the effect of physical activity interventions on CRF can be investigated.
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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) | 0.002 | 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".