Contributors to cancer-related fatigue in childhood cancer survivors and the use of non-pharmacological interventions: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review will aim to identify the domains contributing to cancer-related fatigue in childhood cancer survivors and will explore whether non-pharmacological interventions have addressed these domains. This information will help to better define cancer-related fatigue, identify knowledge gaps in the literature, and direct future research efforts. INTRODUCTION: Cancer-related fatigue is a commonly reported symptom in aftercare following childhood cancer treatments. However, its operational definition and contributors are unclear, which makes it difficult to select targets and design adequate interventions. In this scoping review, we will identify contributing domains to help clarify their role as key characteristics of cancer-related fatigue. We will then review the evidence as to whether these contributing domains have been addressed by non-pharmacological interventions aimed at fatigue. INCLUSION CRITERIA: We will include articles on cancer-related fatigue following childhood cancer treatments (age at diagnosis ≤ 21 years) and non-pharmacological interventions aimed at reducing fatigue. Both will retain qualitative and quantitative studies will be considered for inclusion. METHODS: In accordance with the JBI methodological framework for conducting scoping reviews, we will perform a search in PubMed, PsycINFO, CINAHL, Embase, Cochrane Library, Grey Matters, OAlster, and OpenGrey. We will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. Studies published in English or French will be included, with no date limitations. The data collection and analysis of eligible articles will be performed by two independent reviewers and will be classified in summary tables. The findings on contributors to cancer-related fatigue in childhood cancer survivors will be synthesized in a cross table linking contributor domains with intervention type.
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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 | 0.101 | 0.093 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.075 | 0.015 |
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".