Interventions minimizing fatigue in children/adolescents with cancer: An integrative review
Bibliographic record
Abstract
Fatigue is among the most common, debilitating, and distressing symptoms associated with chronic condition in pediatric population. The purpose of this study was to identify non-pharmacological fatigue interventions in children and adolescents with cancer. For this, we carried out an integrative review of the literature from January 2000 to December 2016. A comprehensive search of four databases was conducted: Cumulative Index to Nursing and Allied Health Literature, Psychology Information, Medline via PubMed, and Web of Science. Randomized controlled trial, quasi-experimental, case-control and cohort studies were included in this review. Thirteen relevant studies were included for analysis. Seven papers reported positive outcomes for exercise, exercise plus leisure activities, healing touch and acupressure. In another six papers using exercise, exercise plus psychological intervention and massage, no effectiveness was found. Effective management of fatigue in children and adolescents is important but research in this area is limited, so the results of this review should be interpreted cautiously. Future researchers are encouraged to test the effective interventions in homogenous cancer populations and in other groups where fatigue is a common concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".