A Practical Approach to Fatigue Management in Colorectal Cancer
Bibliographic record
Abstract
Cancer-related fatigue is serious and complex, as well as one of the most common symptoms experienced by patients with colorectal cancer, with the potential to compromise quality of life, activities of daily living, and ultimately survival. There is a lack of consensus about the definition of cancer-related fatigue; however, definitions have been put forward by the European Association for Palliative Care (EAPC) and the National Comprehensive Cancer Network (NCCN). Numerous cancer- and treatment-related factors can contribute to fatigue, including disease progression, comorbidities, medical complications such as anemia, side effects of other medications, and a number of physical and psychologic factors. This underlines the importance of tackling factors that may contribute to fatigue before reducing the dose of treatment. NCCN guidelines and the EAPC have proposed approaches to managing fatigue in cancer patients; however, relatively few therapeutic agents have been demonstrated to reduce fatigue in randomized controlled trials. It is recognized that physical activity produces many beneficial physiologic modifications to markers of physical performance that can help to counteract various causes of fatigue. In appropriately managed and monitored patients with colorectal cancer, emerging evidence indicates that exercise programs may have a favorable influence on cancer-related fatigue, quality of life, and clinical outcomes, and therefore may help patients tolerate chemotherapy. This review assesses fatigue in patients with colorectal cancer and proposes updates to a treatment algorithm that may help clinicians manage this common problem.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".