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Record W4388571180 · doi:10.1016/j.gie.2023.11.004

Aspirin exposure and its association with metal stent patency in malignant distal biliary obstruction: a large international multicenter propensity score–matched study

2023· article· en· W4388571180 on OpenAlexafffund
Kristina Candido, Simon Bouchard, Christopher Hansen-Barkun, Dora C. Huang, Avijit Chatterjee, Charles Ménard, Corey Miller, Gurpal Sandha, Fergal Donnellan, Jennifer J. Telford, Étienne Désilets, Nauzer Forbes, André Roy, Natalia Causada Calo, S. Ian Gan, Eric Lam, Douglas K. Pleskow, Jeremy Liu Chen Kiow, Avi Sarker, Etienne Cadieux-genesse, Avni Jain, Felix Louis, Mohammad Bilal, Pape-Mamadou Sene, Jehovan Fairclough, Jacqueline Reuangrith, Amine Benmassaoud, Olivia Geraci, Myriam Martel, Yen‐I Chen

Bibliographic record

VenueGastrointestinal Endoscopy · 2023
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsUniversity of CalgaryVancouver General HospitalCentre Hospitalier Universitaire de SherbrookeAlberta Hospital EdmontonVancouver Coastal Health Research InstituteOttawa HospitalUniversity of Alberta HospitalSt. Paul's HospitalProvidence Health CareHôpital Charles-Le MoyneCentre Hospitalier de l’Université de MontréalMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsMedicineStentHazard ratioPropensity score matchingConfidence intervalInternal medicineEndoscopic retrograde cholangiopancreatographyAspirinProportional hazards modelGastroenterologySurgeryRetrospective cohort studyPancreatitis

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.027
GPT teacher head0.261
Teacher spread0.234 · 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 designObservational
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
Published2023
Admission routes2
Has abstractno

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