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Record W4311009115 · doi:10.47967/tor2022col/vol8.01

How common is fatigue across chronic health conditions in children and young people? A systematic review

2022· review· en· W4311009115 on OpenAlexaboutno aff
Kiesha Williams, Maria Loades

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsChronic fatigue syndromeChronic fatigueMedicineChronic conditionPopulationPrevalenceNarrative reviewGerontologyEnvironmental healthPhysical therapyDiseasePathologyIntensive care medicine

Abstract

fetched live from OpenAlex

Fatigue is a common experience for adults with chronic health conditions. Less is understood about the prevalence of fatigue in the paediatric population. This review aimed to synthesise what is known about the point prevalence of fatigue across chronic health conditions within children and young people. Three databases were searched, from January 2000 to July 2021, to identify studies reporting prevalence rates in chronic health conditions in under 18s. Methodological quality was assessed with a modified version of the Newcastle-Ottawa Scale for Cross-Sectional Studies. Twenty-five studies were included. Variations in the assessment of fatigue, across a range of conditions, and heterogenous reporting of prevalence precluded meaningful meta-analysis and so narrative synthesis was completed. Discrepancies in prevalence reports were noted within and across conditions and between child and parent reports but showed fatigue to be more prevalent in those with chronic health conditions compared to healthy peers. Despite discrepancy in prevalence rates of fatigue, some level of fatigue is present across chronic health conditions and tends to be higher in this population compared to healthy peers. Limitations alongside clinical implications and recommendations for future research are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.071
GPT teacher head0.418
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2022
Admission routes1
Has abstractyes

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