Recommendations for the surveillance of cancer-related fatigue in childhood, adolescent, and young adult cancer survivors: a report from the International Late Effects of Childhood Cancer Guideline Harmonization Group
Bibliographic record
Abstract
PURPOSE: Cancer-related fatigue (CRF) negatively affects the lives of childhood, adolescent, and young adult (CAYA) cancer survivors. We aimed to provide an evidence-based clinical practice guideline (CPG) with internationally harmonized CRF surveillance recommendations for CAYA cancer survivors diagnosed < 30 years. METHODS: This CPG was developed by a multidisciplinary panel under the umbrella of the International Late Effects of Childhood Cancer Guideline Harmonization Group. After evaluating concordances and discordances of four existing CPGs, we performed systematic literature searches. We screened articles for eligibility, assessed quality, extracted, and summarized the data from included articles. We formulated recommendations based on the evidence and clinical judgment. RESULTS: Of 3647 articles identified, 70 articles from 14 countries were included. The prevalence of CRF in CAYA cancer survivors ranged from 10-85%. We recommend that healthcare providers are aware of the risk of CRF, implement regular screening with validated measures, and recommend effective interventions to fatigued survivors. CONCLUSIONS: A considerable proportion of CAYA cancer survivors suffers from CRF even years after the end of treatment. IMPLICATIONS FOR CANCER SURVIVORS: We recommend that healthcare providers adopt regular screening to detect and treat CRF early and positively influence survivors' health and quality of life.
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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 | 0.040 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".