Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes and highlights recent advances in current knowledge of the relationship between coffee and caffeine consumption and risk of coronary heart disease. Potential mechanisms and genetic modifiers of this relationship are also discussed. RECENT FINDINGS: Studies examining the association between coffee consumption and coronary heart disease have been inconclusive. Coffee is a complex mixture of compounds that may have either beneficial or harmful effects on the cardiovascular system. Randomized controlled trials have confirmed the cholesterol-raising effect of diterpenes present in boiled coffee, which may contribute to the risk of coronary heart disease associated with unfiltered coffee consumption. A recent study examining the relationship between coffee and risk of myocardial infarction incorporated a genetic polymorphism associated with a slower rate of caffeine metabolism and provides strong evidence that caffeine also affects risk of coronary heart disease. Several studies have reported a protective effect of moderate coffee consumption, which suggests that coffee contains other compounds that may be beneficial. SUMMARY: Diterpenes present in unfiltered coffee and caffeine each appear to increase risk of coronary heart disease. A lower risk of coronary heart disease among moderate coffee drinkers might be due to antioxidants found in coffee.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".