Rationale, design, and preliminary results of the Iran-premature coronary artery disease study (I-PAD): A multi-center case-control study of different Iranian ethnicities.
Bibliographic record
Abstract
BACKGROUND: Premature coronary artery disease (CAD) is still prevalent worldwide and may differ in various ethnicities. Due to the presence of different ethnicities in Iran, the Iran-premature coronary artery disease (I-PAD) study aimed to determine the frequency of premature CAD and related risk factors based on each ethnicity. METHODS: In this multi-center case-control study, 4000 patients with premature CAD from ten different ethnicities who lived in different cities of Iran and underwent coronary angiography were enrolled (women aged ≤ 70 and men ≤ 60 years). Patients with CAD defined as obstruction equal or above 75% in at least a single coronary artery or left main ≥ 50% were included in the case group, while patients with normal coronary arteries were included in the control group. Lifestyle behaviors, cardiometabolic risk factors, anthropometric measurements, and other variables were collected. Serum, whole blood, buffy coat, plasma, urine, stool, and saliva samples were stored. RESULTS: The number of patients enrolled until April 2020 was 2071. The mean age of patients was 53.51 ± 7.52 and 934 (45.09%) of patients were women. To date, about 39.6% of the patients were normal. Also, about 26.0% were with one-vessel disease (1VD), 15.0% with two-vessel disease (2VD), and 15.2% with three-vessel disease (3VD). More than 30000 patients' biosamples from across the country have been stored. CONCLUSION: Knowing the frequency of premature CAD according to different ethnicities with major differences in their lifestyle behaviors and risk factors can assist health decision-makers. In addition, I-PAD biosamples will be an invaluable source.
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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".