Screening, Assessment, and Management of Fatigue in Adult Survivors of Cancer: An American Society of Clinical Oncology Clinical Practice Guideline Adaptation
Bibliographic record
Abstract
PURPOSE: This guideline presents screening, assessment, and treatment approaches for the management of adult cancer survivors who are experiencing symptoms of fatigue after completion of primary treatment. METHODS: A systematic search of clinical practice guideline databases, guideline developer Web sites, and published health literature identified the pan-Canadian guideline on screening, assessment, and care of cancer-related fatigue in adults with cancer, the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines In Oncology (NCCN Guidelines) for Cancer-Related Fatigue and the NCCN Guidelines for Survivorship. These three guidelines were appraised and selected for adaptation. RESULTS: It is recommended that all patients with cancer be evaluated for the presence of fatigue after completion of primary treatment and be offered specific information and strategies for fatigue management. For those who report moderate to severe fatigue, comprehensive assessment should be conducted, and medical and treatable contributing factors should be addressed. In terms of treatment strategies, evidence indicates that physical activity interventions, psychosocial interventions, and mind-body interventions may reduce cancer-related fatigue in post-treatment patients. There is limited evidence for use of psychostimulants in the management of fatigue in patients who are disease free after active treatment. CONCLUSION: Fatigue is prevalent in cancer survivors and often causes significant disruption in functioning and quality of life. Regular screening, assessment, and education and appropriate treatment of fatigue are important in managing this distressing symptom. Given the multiple factors contributing to post-treatment fatigue, interventions should be tailored to each patient's specific needs. In particular, a number of nonpharmacologic treatment approaches have demonstrated efficacy in 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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".