Bibliographic record
Abstract
2 Unveiling the causes of heart disease in China Despite more than 80 % of the global burden of cardiovascular diseases now occurring in low and middle income countries,1 most of what we know about the causes of these diseases comes from studies of populations in North America and western Europe. Many of the resulting clinical and public health policies in these high income regions are being applied in lower income regions, but perhaps sometimes inappropriately, since many lower income countries have physical, social and economic environments that are substantially different from those in richer parts of the world. Helping to close this gulf in cardiovascular knowledge is the INTERHEART case-control study. Spread across one quarter of the world’s countries, including 30 or so lower and middle income countries, the study is a unique resource for assessing causes of coronary heart disease in different contexts.2-5 For this reason, and because of its sheer size (12 000 cases of acute myocardial infarction [MI] and 15 000 controls), INTERHEART has rightly become a landmark study. New INTERHEART findings for China are unveiled in this edition of the journal.6 The 12
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.558 | 0.276 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".