Prevalence and Factors Associated With Cancer‐Related Fatigue Among Children and Adolescents Undergoing Cancer Treatment: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: The literature on fatigue in children and adolescents undergoing cancer treatment is highly variable, creating uncertainties about its prevalence and identifying those at higher risk. OBJECTIVES: The primary purpose was to describe the prevalence of fatigue among patients (< 21 years) undergoing cancer treatment across cancer types. Secondary outcomes included the prevalence of severe fatigue and factors associated with fatigue. METHODS: Systematic searches of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO were conducted from inception to May 22, 2023. Two reviewers independently identified relevant citations and extracted data. Pooled prevalence estimates were derived using an inverse variance, random-effects model. We used Joanna Briggs's critical appraisal checklist to assess study quality. (PROSPERO: CRD42020179307). RESULTS: 98%; 8 studies; 1027 patients]). Subgroup analyses based on cancer type, study design, fatigue scale, fatigue reporting personnel, sample frame, and response rate did not reveal significant differences in fatigue prevalence. Fatigue prevalence significantly differed by treatment setting (inpatient [83%] vs. outpatient [55%] vs. inpatient and outpatient [69%]; p: 0.02). Due to considerable heterogeneity among studies, data on fatigue-associated factors are presented descriptively. CONCLUSIONS: The prevalence of fatigue among children and adolescents undergoing cancer treatment is variable but notably high. Systematic evaluation of factors associated with fatigue is essential to understanding which children are at high risk of developing fatigue. TRIAL REGISTRATION: PROSPERO: CRD42020179307.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".