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Record W3161422101 · doi:10.1136/heartjnl-2020-318929

Coronary artery disease in East and South Asians: differences observed on cardiac CT

2021· review· en· W3161422101 on OpenAlexaff
Alexander Chua, Daniel Adams, Damini Dey, Ron Blankstein, Timothy Fairbairn, Jonathon Leipsic, Abdul Rahman Ihdayhid, Brian Ko

Bibliographic record

VenueHeart · 2021
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCoronary artery diseaseCardiologyInternal medicineCalcificationStenosisCardiac imagingEpidemiologyCardiac surgeryCoronary arteriesRadiologyArtery

Abstract

fetched live from OpenAlex

Epidemiological studies have observed East Asians (EAs) are significantly less likely to develop or die from coronary artery disease (CAD) compared with Caucasians. Conversely South Asians (SAs) develop CAD at higher rate and earlier age. Recently, a range of features derived from cardiac CT have been identified which may further characterise ethnic differences in CAD. Emerging data suggest EAs exhibit less coronary calcification and high-risk, non-calcified plaque compared with Caucasians on CT, with no difference in luminal stenosis. In contrast, SAs exhibit similar to higher coronary calcification and luminal stenosis, smaller luminal dimensions and more high-risk, non-calcified plaque than Caucasians. Beyond demonstrating ethnic differences in CAD, cardiac CT may enhance and individualise cardiovascular risk stratification in EAs and SAs. While data thus far in EAs have demonstrated calcium score and CT-derived luminal stenosis may incrementally predict cardiovascular risk beyond traditional risk scores, there remains a paucity of data assessing its use in SAs. Future studies may clarify the prognostic value of cardiac CT in SAs and investigate how this modality may guide preventative therapy and coronary intervention of CAD in EAs and SAs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.665
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.319
Teacher spread0.220 · 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 teacher head, not a consensus.

Study designObservational
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

Citations14
Published2021
Admission routes1
Has abstractyes

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