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Record W2883055530 · doi:10.1016/s2213-8587(18)30205-5

Association of preoperative glucose concentration with myocardial injury and death after non-cardiac surgery (GlucoVISION): a prospective cohort study

2018· article· en· W2883055530 on OpenAlexafffund
Zubin Punthakee, Pilar Paniagua Iglesias, Pablo Alonso‐Coello, Ignasi Gich, Inmaculada India, Germán Málaga, Ruben Diaz Jover, Hertzel C. Gerstein, P.J. Devereaux

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersJanssen PharmaceuticalsDepartment of SurgeryNational Institute of General Medical SciencesOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchDepartment of Medicine, Georgetown UniversityNational Center for Research ResourcesEli Lilly CanadaGeneral Research Fund of Shanghai Normal UniversityNational Institutes of HealthNovo NordiskMerckDiagnostic Services ManitobaNational Center for Advancing Translational SciencesUniversity of ManitobaMcMaster UniversityDepartment of Anesthesiology, Medical College of WisconsinHeart and Stroke Foundation of CanadaManitoba Health Research CouncilHamilton Health SciencesUniversiti MalayaManitoba Medical Service FoundationAmgenBoehringer IngelheimPfizerBristol-Myers Squibb CanadaInstitute of Clinical and Translational SciencesSanofiAmerican Heart AssociationAbbott LaboratoriesAstraZeneca
KeywordsMedicineDiabetes mellitusProspective cohort studyPerioperativeHazard ratioOdds ratioCardiac surgeryLogistic regressionInternal medicineCohortSurgeryEndocrinologyConfidence interval

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)0.003
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.260
Teacher spread0.251 · 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

Citations43
Published2018
Admission routes2
Has abstractno

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