Prevalence of cancer-related fatigue in a population-based sample of colorectal, breast, and prostate cancer survivors.
Bibliographic record
Abstract
9131 Background: Cancer-related fatigue (CRF) is the most prevalent and distressing cancer-related symptom and has a greater negative impact on patients' daily activities and quality of life than other cancer-related symptoms, including pain and depression. However, the prevalence and severity of persistent CRF and related disability in the post-treatment survivorship period has seldom been examined in populations other than breast cancer. The primary objective of the study was to describe the prevalence of significant CRF and associated levels of disability in a mixed cancer population sample at 3 time points in the post-treatment survivorship trajectory. Methods: Based on cancer registry data, a self-administered mail based questionnaire using Dillman's Tailored Design Method was sent to 3 cohorts of disease-free cancer survivors (6-18 months; 2-3 years; and 5-6 years post-treatment) previously treated for non-metastatic breast, prostate or colorectal cancer. Fatigue was measured using the FACT-F and disability was measured with the WHO-Disability Assessment Schedule. Clinical information was extracted from chart review. Frequencies of significant fatigue by disease sites and time points were studied and compared using chi-square test. Disability between those with and without CRF was also compared using Cochran-Armitage trend test. Results: 1294 questionnaire packages were completed (63% response rate). The FACT-F score was 39.1+10.9; 29% (95% CI: [27%, 32%]) of the sample reported significant fatigue (FACT-F≤34) and this was associated with much higher levels of disability (p<0.0001). Breast (40% [35%, 44%]) and colorectal (33% [27%, 38%]) survivors had significantly higher rates of fatigue (≤34) compared to the prostate group (17% [14%, 21%]) (p<0.0001). Fatigue levels remained relatively stable across the 3 time points. Conclusions: CRF was a significant and debilitating symptom for a substantial minority of the respondents across all 3 time points. Effective CRF management strategies are needed and have the potential to significantly reduce morbidity associated with cancer and its treatments and to improve quality of life for the growing population of cancer survivors.
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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 | 0.001 |
| 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".