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Record W4393255859 · doi:10.21203/rs.3.rs-4138973/v1

Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020

2024· preprint· en· W4393255859 on OpenAlexafffund
Luis Gabriel Cuervo, Carmen Juliana Villamizar, Daniel Cuervo, Pablo Zapata, Maria B. Ospina, Sara Marcela Valencia, Alfredo Polo, Ángela Suárez, María Olga Bula, J. Jaime Miranda, Gynna Millán, Diana Elizabeth Cuervo, Nancy J. Owens, Felipe Piquero, Janet Hatcher-Roberts, Gabriel Dario Paredes, María Fernanda Navarro, Ingrid Liliana Minotta, Carmen Palta, Eliana Martínez‐Herrera, Ciro Jaramillo Molina

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsBruyèreUniversity of OttawaQueen's University
FundersNational Cancer InstituteFogarty International CenterNational Institute of Mental HealthFondo Nacional de Desarrollo Científico, Tecnológico y de Innovación TecnológicaBiotechnology and Biological Sciences Research CouncilMedical Research CouncilInter-American Institute for Global Change ResearchConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaWorld Diabetes FoundationNational Science FoundationGrand Challenges CanadaAlliance for Health Policy and Systems ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungInternational Development Research CentreUniversity of North Carolina at Chapel HillBloomberg PhilanthropiesEngineering and Physical Sciences Research CouncilUK Research and Innovation
KeywordsEquity (law)Big dataBusinessOpen dataCross-sectional dataRegional scienceGeographyComputer scienceWorld Wide WebEconomicsPolitical scienceData miningEconometrics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.498
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.584
GPT teacher head0.640
Teacher spread0.056 · 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 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

Citations0
Published2024
Admission routes2
Has abstractno

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