Physiological correlates of cancer-related fatigue in advanced non-small cell lung cancer patients
Bibliographic record
Abstract
Background. Fatigue is a debilitating consequence of lung cancer and its treatments. Reviewing the literature in cancer-related fatigue provides few etiological factors, making evidence-based interventions limited. In this study, previously identified factors as well as muscular and cardiorespiratory function were assessed as potential contributors to fatigue in stages 3A/B and 4 NSCLC patients. Methods. Participants were evaluated by a physical therapist within the McGill Cancer Nutrition and Rehabilitation Program. Performance-based measures of physical function [upper limb strength and endurance (Jamar dynamometry), lower limb strength (30sec chair rise), cardiorespiratory function (2 minute walk - 2MW)] and a symptom questionnaire (Edmonton Symptom Assessment Scale) were conducted at one point in time. The primary endpoint of global fatigue rating was assessed using the Brief Fatigue Inventory (BFI). Results. Fifty-eight patients (30M:28F, mean age 68 +/- 12) participated in the study. Forty-three percent were actively receiving treatment at the time of assessment. On the BFI, 67% had moderate or severe fatigue and 84% indicated fatigue had interfered with their functioning during the past 24 hours. Global fatigue scores were unrelated to hand grip strength or endurance measurements, hematological parameters or sleep quality but were significantly correlated with chair rise performance, overall rating of breathlessness, patient rating of pain and patient rated weakness. Multivariate regression analysis suggested the best model for global fatigue scores incorporates patients' ratings of weakness, breathlessness and chair rise performance. Conclusions. Fatigue is prevalent and impacts on the function of advanced NSCLC patients. Several key factors contribute to this fatigue, with muscular and cardiorespiratory restrictions playing an important role. Such findings may have clinical implications in the recommendations of rest and exercise to best manage fatigue.
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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 | 0.003 |
| 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".