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Record W2900898116 · doi:10.1016/j.canep.2018.10.006

Alcohol consumption and lung cancer risk: A pooled analysis from the International Lung Cancer Consortium and the SYNERGY study

2018· article· en· W2900898116 on OpenAlexafffund
Darren R. Brenner, Gord Fehringer, Zuo‐Feng Zhang, Yuan-Chin Amy Lee, Travis J. Meyers, Keitaro Matsuo, Hidemi Ito, Paolo Vineis, Isabelle Stücker, Paolo Boffetta, Paul Brennan, David C. Christiani, Nancy Diao, Yun‐Chul Hong, Maria Teresa Landi, Hal Morgenstern, Ann G. Schwartz, Gad Rennert, Walid Saliba, Curtis C. Harris, Irene Orlow, Juan Miguel Barros-Dios, Alberto Ruano‐Raviña, Jack Siemiatycki, Anita Koushik, Michele L. Coté, Philip Lazarus, Guillermo Fernández‐Tardón, Adonina Tardón, Loı̈c Le Marchand, Hermann Brenner, Kai-Uwe Saum, Eric J. Duell, Angeline S. Andrew, Dario Consonni, Ann Olsson, Kurt Straíf

Bibliographic record

VenueCancer Epidemiology · 2018
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsPublic Health OntarioLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalCentre Hospitalier de l’Université de MontréalAlberta Health Services
FundersNational Center for Research ResourcesCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchNational Cancer InstituteNational Institutes of HealthUniversidad de OviedoDeutsche Gesetzliche UnfallversicherungMinistry of Health, Labour and WelfareMinistry of Education, Culture, Sports, Science and TechnologyWorld Health Organization
KeywordsMedicineLung cancerAlcohol consumptionCancerOncologyPooled analysisConsumption (sociology)LungEnvironmental healthInternal medicineMeta-analysisAlcohol

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)0.007
metaresearch head score (Gemma)0.011
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.018
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.098
GPT teacher head0.455
Teacher spread0.357 · 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

Citations39
Published2018
Admission routes2
Has abstractno

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