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Record W3087061909 · doi:10.7189/jogh.10.020352

Modeling the future of cancer registration and research: The Martinique Cancer Data Hub Platform

2020· review· en· W3087061909 on OpenAlexfundno aff
Clarisse Joachim, Mylène Vestris, Miguelle Marous, Thierry Almont, Stephen Ulric-Gervaise, Moustapha Dramé, Cédric Contaret, Juliette Smith‐Ravin, Patrick Escarmant, Emmanuelle Sylvestre, Jacqueline Véronique-Baudin

Bibliographic record

VenueJournal of Global Health · 2020
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersEuropean Regional Development FundUniversity Hospital of MartiniqueInstitute of Cancer ResearchInstitut National Du CancerPublic Health AgencyCentre International de Recherche sur le Cancer
KeywordsInteroperabilityComputer scienceTelemedicineHealth informaticseHealthHealth careBig dataData scienceInformation and Communications TechnologyPopulationPublic healthKnowledge managementWorld Wide WebMedicineNursingPolitical scienceData mining

Abstract

fetched live from OpenAlex

T he number of Clinical Data Research Networks (CDRN) focused on creating large collections of data from multiple digital sources, has vastly increased in the last few years. These CDRN are particularly relevant in outlying island regions, where they make it possible for those involved to communicate and contribute, thereby facilitating remote collaborations, despite the physical distance.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.527
GPT teacher head0.585
Teacher spread0.058 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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