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Record W2791412237 · doi:10.1177/1367493517752498

Interventions minimizing fatigue in children/adolescents with cancer: An integrative review

2018· review· en· W2791412237 on OpenAlexaff
Michelle Darezzo Rodrigues Nunes, Emiliana de Omena Bomfim, Kärin Olson, Luís Carlos Lopes‐Júnior, Fernanda Machado Silva‐Rodrigues, Regina Aparecida Garcia de Lima, Lucila Castanheira Nascimento

Bibliographic record

VenueJournal of Child Health Care · 2018
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of AlbertaUniversity of Saskatchewan
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMedicinePsychological interventionAcupressureRandomized controlled trialMEDLINEMassagePhysical therapyPopulationIntervention (counseling)Alternative medicineFamily medicinePsychiatrySurgery

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.471
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations45
Published2018
Admission routes1
Has abstractyes

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