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Record W2476718374 · doi:10.1161/jaha.116.003941

Impact of South Asian Ethnicity on Long‐Term Outcomes After Coronary Artery Bypass Grafting Surgery: A Large Population‐Based Propensity Matched Study

2016· article· en· W2476718374 on OpenAlexafffundabout
Saswata Deb, Jack V. Tu, Peter C. Austin, Dennis T. Ko, Rodolfo V. Rocha, C. David Mazer, Alex Kiss, Stephen E. Fremes

Bibliographic record

VenueJournal of the American Heart Association · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreSunnybrook HospitalUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersHospital for Sick ChildrenSociety of Thoracic SurgeonsHeart and Stroke Foundation of Canada
KeywordsMedicinePropensity score matchingHazard ratioInternal medicineMyocardial infarctionCardiologyStroke (engine)PopulationCoronary artery diseaseDiabetes mellitusSurgeryConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Ethnicity is an important predictor of coronary artery bypass graft surgery (CABG) outcomes. South Asians (SA), one of the largest ethnic groups with a high burden of cardiovascular disease, are hypothesized to have inferior outcomes after CABG compared to other ethnic groups. Given the paucity and controversy of literature in this area, the objective of this study was to examine the impact of SA versus the general population (GP) on long-term outcomes following CABG. METHOD AND RESULTS: Using administrative databases and a surname algorithm, 83 850 patients (SA: 2653, GP: 81 197) who underwent isolated CABG in Ontario, Canada from 1996 to 2007 were identified; mean follow-up was 9.1±3.9 years. SA were younger (SA: 61.7±9.4, GP: 64.1±10.0 years, standardized difference=0.25) with more cardiac risk factors, including diabetes (SA: 54.1%, GP: 34.9%, standardized difference =0.40). Propensity-score matching resulted in 2473 matched pairs between SA and GP with all baseline covariates being balanced (standardized difference <0.1). Being a SA compared to the GP was protective against freedom from major adverse cardiac and cerebrovascular events, defined by all-cause death, myocardial infarction, stroke, or coronary reintervention: Adjusted Cox-proportional hazard ratio 0.91, 95% CI (0.83-0.99), adjusted-P=0.04; this was also true for freedom from all-cause mortality: hazard ratio 0.81, 95% CI (0.72-0.91), adjusted P=0.0004. The adjusted proportion of major adverse cardiac and cerebrovascular events was lower in the SA (SA: 34.7%, GP: 37.8%, McNemar P=0.03), driven largely by all-cause mortality (SA: 20.4%, GA: 24.3%, McNemar P=0.001). CONCLUSIONS: Contrary to existing notions, our study finds that being a SA is protective with respect to freedom from long-term major adverse cardiac and cerebrovascular events and mortality after CABG. More studies are required to corroborate and explore causal factors of these findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.360
Teacher spread0.315 · 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

Citations18
Published2016
Admission routes3
Has abstractyes

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