Fatigue and Physical Activity in Older Patients With Cancer: A Six-Month Follow-Up Study
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To determine the relationship between fatigue and physical activity in older patients with cancer. DESIGN: Targeted analysis using data from a prospective longitudinal study. SETTING: A cancer care facility in southeastern Ontario, Canada. SAMPLE: 440 patients, aged 65 years and older, seeking consultation for cancer treatment at a regional cancer clinic for lymphoma or leukemia or lung, breast, genitourinary, head or neck, gastrointestinal, or skin cancers. METHODS: Self-report questionnaires were mailed to consenting participants and completed at baseline and three and six months after consultation for cancer treatment. MAIN RESEARCH VARIABLES: Participants rated fatigue and physical activity and reported comorbidities and personal demographic characteristics. Clinical measures of disease and treatment factors were obtained through chart abstraction. FINDINGS: Fatigue was the most prevalent symptom reported. Higher fatigue was associated with lower physical activity levels. Physical activity level significantly predicted fatigue level, regardless of age. CONCLUSIONS: Physical activity level is a modifiable factor significantly predicting cancer-related fatigue at three and six months following consultation for cancer treatment. The results suggest that physical activity may reduce fatigue in older patients with cancer. IMPLICATIONS FOR NURSING: Physical activity interventions should be developed and tested in older patients with cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".