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Record W1975753574 · doi:10.1053/j.gastro.2011.11.019

Nonsteroidal Anti-inflammatory Drug Use Reduces Risk of Adenocarcinomas of the Esophagus and Esophagogastric Junction in a Pooled Analysis

2011· review· en· W1975753574 on OpenAlexafffund
Linda M. Liao, Thomas L. Vaughan, Douglas A. Corley, Michael B. Cook, Alan G. Casson, Farin Kamangar, Christian C. Abnet, Harvey A. Risch, Carol Giffen, Neal D. Freedman, Wong‐Ho Chow, Shahram Sadeghi, Nirmala Pandeya, David C. Whiteman, Liam Murray, Leslie Bernstein, Marilie D. Gammon, Anna H. Wu

Bibliographic record

VenueGastroenterology · 2011
Typereview
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of Environmental Health SciencesCancer Council QueenslandNational Health and Medical Research CouncilEconomic and Social Research CouncilNational Cancer InstituteNational Institutes of HealthNova Scotia Health Research Foundation
KeywordsMedicineAspirinOdds ratioInternal medicineConfidence intervalBarrett's esophagusGastroenterologyAdenocarcinomaPopulationLogistic regressionRisk factorCancer

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)0.002
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.009
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.028
GPT teacher head0.293
Teacher spread0.265 · 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
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

Citations159
Published2011
Admission routes2
Has abstractno

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