Screening, evaluation, and management of cancer‐related fatigue: Ready for implementation to practice?
Bibliographic record
Abstract
Answer questions and earn CME/CNE Evidence regarding cancer-related fatigue (fatigue) has accumulated sufficiently such that recommendations for screening, evaluation, and/or management have been released recently by 4 leading cancer organizations. These evidence-based fatigue recommendations are available for clinicians, and some have patient versions; but barriers at the patient, clinician, and system levels hinder dissemination and implementation into practice. The underlying biologic mechanisms for this debilitating symptom have not been elucidated completely, hindering the development of mechanistically driven interventions. However, significant progress has been made toward methods for screening and comprehensively evaluating fatigue and other common symptoms using reliable and valid self-report measures. Limited data exist to support the use of any pharmacologic agent; however, several nonpharmacologic interventions have been shown to be effective in reducing fatigue in adults. Never before have evidence-based recommendations for fatigue management been disseminated by 4 premier cancer organizations (the National Comprehensive Cancer, the Oncology Nursing Society, the Canadian Partnership Against Cancer/Canadian Association of Psychosocial Oncology, and the American Society of Clinical Oncology). Clinicians may ask: Are we ready for implementation into practice? The reply: A variety of approaches to screening, evaluation, and management are ready for implementation. To reduce fatigue severity and distress and its impact on functioning, intensified collaborations and close partnerships between clinicians and researchers are needed, with an emphasis on system-wide efforts to disseminate and implement these evidence-based recommendations.
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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 | 0.021 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.030 | 0.027 |
| Insufficient payload (model declined to judge) | 0.044 | 0.030 |
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".