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Record W3047246856 · doi:10.1016/j.bja.2020.06.051

Bleeding Independently associated with Mortality after noncardiac Surgery (BIMS): an international prospective cohort study establishing diagnostic criteria and prognostic importance

2020· article· en· W3047246856 on OpenAlexafffund
Pavel S Roshanov, John W. Eikelboom, Daniel I. Sessler, Clive Kearon, Gordon Guyatt, Mark Crowther, Vikas Tandon, Flávia K. Borges, André Lamy, Richard Whitlock, Bruce Biccard, Wojciech Szczeklik, Mohamed Panju, Jessica Spence, Amit X. Garg, Michael McGillion, Tomas VanHelder, Peter A. Kavsak, Justin de Beer, Mitchell Winemaker, Yannick Le Manach, Tej Sheth, Jehonathan H. Pinthus, Deborah Siegal, Lehana Thabane, Marko Šimunović, Ryszard Mizera, Sebastián Ribas, P.J. Devereaux

Bibliographic record

VenueBritish Journal of Anaesthesia · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt. Joseph’s Healthcare HamiltonHealth Sciences CentreInstitute for Clinical Evaluative SciencesImpactThrombosis and Atherosclerosis Research InstituteLondon Health Sciences CentreMcMaster UniversityPopulation Health Research Institute
FundersDaiichi Sankyo EuropeJanssen PharmaceuticalsJanssen BiotechInstituto de Salud Carlos IIICanadian Institutes of Health ResearchPhilips Oral HealthcareBristol-Myers Squibb CanadaCovidienFundación Cardioinfantil - Instituto de CardiologíaUniversidad Autónoma de BucaramangaNational Health and Medical Research CouncilUniversiti MalayaResearch Grants Council, University Grants CommitteeMinistério da SaúdeDiagnostic Services ManitobaUniversité Pierre et Marie CurieAustralian and New Zealand College of AnaesthetistsMcMaster UniversitySiemens Medical Solutions USADaiichi-SankyoRoche DiagnosticsHeart and Stroke Foundation of CanadaManitoba Health Research CouncilOctapharmaPopulation Health Research InstituteHamilton Health SciencesDepartment of Surgery, University of ManitobaBayer CorporationAlexion PharmaceuticalsWinnipeg FoundationRocheBeckman Coulter FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoGlaxoSmithKlinePfizerUniversity of ManitobaMedical Research CouncilFundació la Marató de TV3Abbott DiagnosticsLEO PharmaLEO Pharma Research FoundationAbbott LaboratoriesBayerChinese University of Hong KongEli Lilly and CompanySanofi-Aventis Korea CompanyOrtho Clinical DiagnosticsAstraZenecaStrykerGeneral Research Fund of Shanghai Normal UniversityInyuvesi Yakwazulu-NataliAmerican Heart AssociationManitoba Medical Service FoundationBoehringer Ingelheim
KeywordsMedicineHazard ratioProspective cohort studyConfidence intervalProportional hazards modelCohortCohort studyInternal medicineSurgery

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.018
GPT teacher head0.268
Teacher spread0.250 · 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

Citations84
Published2020
Admission routes2
Has abstractno

Explore more

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