Coronary artery disease in East and South Asians: differences observed on cardiac CT
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".