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Record W2112657305 · doi:10.1016/s0140-6736(14)62038-9

Global surveillance of cancer survival 1995–2009: analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2)

2014· article· en· W2112657305 on OpenAlexfundno aff
Claudia Allemani, Hannah K. Weir, Helena Carreira, Rhea Harewood, Devon Spika, Xiao-Si Wang, Finian Bannon, Jane Ahn, Christopher J. Johnson, Audrey Bonaventure, Rafael Marcos‐Gragera, Charles Stiller, Gulnar Azevedo e Silva, Wanqing Chen, Olufemi J. Ogunbiyi, Bernard Rachet, Matthew Soeberg, Hui You, Tomohiro Matsuda, Magdalena Bielska‐Lasota, Hans H. Storm, T. C. Tucker, Michel P. Coleman

Bibliographic record

VenueThe Lancet · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersBC Cancer AgencyPartenariat Canadien Contre Le CancerInstitut National Du CancerUniversitat de ValènciaCenters for Disease Control and PreventionSwiss ReCancer Institute NSWNational Institute for Health and Care ResearchCancer Care OntarioRegione ToscanaOhio Department of HealthSwiss Cancer Research FoundationInternational Atomic Energy AgencyPennsylvania Department of HealthNew York City Department of Health and Mental HygieneACT GovernmentNational Cancer InstituteUniversity of KentuckyWorld Bank GroupCancer Research UKLondon School of Hygiene and Tropical MedicineNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsCancer registryMedicineCancerPopulationMetric (unit)Environmental healthDemographyInternal medicineBusiness

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.011
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.130
GPT teacher head0.386
Teacher spread0.256 · 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 designMeta-analysis
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

Citations2,662
Published2014
Admission routes1
Has abstractno

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