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Record W2081717606 · doi:10.1371/journal.pmed.1001656

Pediatric Oncology as the Next Global Child Health Priority: The Need for National Childhood Cancer Strategies in Low- and Middle-Income Countries

2014· article· en· W2081717606 on OpenAlexaff
Sumit Gupta, Roberto Rivera‐Luna, Raul C. Ribeiro, Scott C. Howard

Bibliographic record

VenuePLoS Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsLow and middle income countriesChildhood cancerGlobal healthMedicinePediatric oncologyChild healthDeveloping countryLow incomePediatric cancerChild survivalCancerHealthcare systemEconomic growthFamily medicineGerontologyChild mortalityEnvironmental healthPublic healthHealth careSocioeconomicsNursingPopulationSociologyInternal medicineEconomics

Abstract

fetched live from OpenAlex

N As is already the case in high-income countries, cancer represents the leading cause of non-accidental death among children in a growing number of middleincome countries N Meaningful declines in global childhood cancer mortality will require moving beyond the current situation through the establishment of national childhood cancer strategies N Key components of such strategies include financial coverage, accreditation of childhood cancer centers, mandatory childhood cancer reporting and registration, development of national standards of care, and the creation of national childhood cancer governing bodies N Challenges to implementing such strategies include a paucity of implementa- tion research, formal policy evaluation, and costing data N The ideal structure of such strategies in low-income countries is currently unknown, given severe resource constraints, deficits in infrastructure, and competing health needs

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.354
Teacher spread0.315 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations130
Published2014
Admission routes1
Has abstractyes

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