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Record W4399760949 · doi:10.1016/j.ejcped.2024.100171

Inequities in childhood cancer research: A scoping review

2024· review· en· W4399760949 on OpenAlexaff
Jean Hunleth, Sarah Burack, Lindsey Kaufman, Caroline Mohrmann, Thembekile Shato, Eric M. Wiedenman, Janet Njelesani

Bibliographic record

VenueEJC Paediatric Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
FundersAlvin J. Siteman Cancer CenterNational Cancer InstituteFoundation for Barnes-Jewish HospitalNational Institutes of HealthAmerican Occupational Therapy Foundation
KeywordsChildhood cancerCancerMedicine

Abstract

fetched live from OpenAlex

An integral part of understanding and then designing programs to reduce childhood cancer inequities includes adequate representation of people with cancer in research, including children. A scoping review was carried out to understand how cancer research is oriented toward inequities and to identify who has participated in childhood qualitative cancer research. A systematic search identified 119 qualitative studies that met inclusion criteria, with most studies taking place in high-income countries (n=84). Overall, data were lacking on social determinants of health at multiple levels-structural, household, child, and guardian. Only 29 studies reported on race and/or ethnicity, with the majority of those including predominantly or all white children. Six articles included socioeconomic information, and across most articles, attention was absent to the financial ramifications of cancer care. Limited reporting of sociodemographics highlights a broader issue of neglecting key demographics and social factors that contribute to inequities.

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)0.016
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.053
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0200.022
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.262
GPT teacher head0.550
Teacher spread0.288 · 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.

Study designSystematic review
DomainEvaluation
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

Citations6
Published2024
Admission routes1
Has abstractyes

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