MétaCan
Menu
Back to cohort
Record W4391930281 · doi:10.1097/cej.0000000000000874

The global gastric cancer consortium: an update from the Stomach cancer Pooling (StoP) project

2024· article· en· W4391930281 on OpenAlexfundno aff
Claudio Pelucchi, Carlo La Vecchia, Rossella Bonzi, Eva Negri, Giovanni Corso, Stefania Boccia, Paolo Boffetta, M. Constanza Camargo, María Paula Curado, Nuno Lunet, Jesús Vioqué, Zuo‐Feng Zhang

Bibliographic record

VenueEuropean Journal of Cancer Prevention · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
FundersUniversidad de LeónNational Institutes of Biomedical Innovation, Health and NutritionHellenic Health FoundationUniversità Cattolica del Sacro CuoreLatvijas UniversitateUniversidad Nacional Autónoma de MéxicoAssociazione Italiana per la Ricerca sul CancroStony Brook UniversityUniversity of GlasgowKarolinska InstitutetMinistero della SaluteHarbin Medical UniversityPeking UniversityUniversidade do PortoUniversity of LeedsUniversity of Ottawa
KeywordsMedicineCancerFamily historyStomach cancerEnvironmental healthSocioeconomic statusStomachRed meatInternal medicinePathology

Abstract

fetched live from OpenAlex

We updated to December 2023 the main findings of the stomach cancer pooling (StoP) project including about 13 000 cases and 31 000 controls from 29 case-control and 5 nested studies. The StoP project quantified more precisely than previously available the positive associations of tobacco smoking, high alcohol consumption, meat intake, selected occupations (e.g. agricultural and miners), gastric ulcer and family history with gastric cancer and the inverse associations with socioeconomic status and selected aspects of diet (fruits, including citrus fruits, vegetables, including allium and mushrooms, and polyphenols). No consistent associations were found with coffee, yoghurt and leisure-time physical activity, metformin or proton pump inhibitors use.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.353
Teacher spread0.322 · 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 designOther design
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

Citations14
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Cancer PreventionSame topicColorectal Cancer Screening and DetectionFrench-language works237,207