Bibliographic record
Abstract
Lifestyle is far more important than most physicians suppose. Dietary changes in China that have resulted from increased prosperity are probably responsible for a marked rise in coronary risk in the past several decades, accelerating in recent years. Intake of meat and eggs has increased, while intake of fruits, vegetables and whole grains has decreased. Between 2003 and 2013, coronary mortality in China increased 213%, while stroke mortality increased by 26.6%. Besides a high content of cholesterol, meat (particularly red meat) contains carnitine, while egg yolks contain phosphatidylcholine. Both are converted by the intestinal microbiome to trimethylamine, in turn oxidised in the liver to trimethylamine n-oxide (TMAO). TMAO causes atherosclerosis in animal models, and in patients referred for coronary angiography high levels after a test dose of two hard-boiled eggs predicted increased cardiovascular risk. The strongest evidence for dietary prevention of stroke and myocardial infarction is with the Mediterranean diet from Crete, a nearly vegetarian diet that is high in beneficial oils, whole grains, fruits, vegetables and legumes. Persons at risk of stroke should avoid egg yolk, limit intake of red meat and consume a diet similar to the Mediterranean diet. A crucial issue for stroke prevention in China is reduction of sodium intake. Dietary changes, although difficult to implement, represent an important opportunity to prevent stroke and have the potential to reverse the trend of increased cardiovascular risk in China.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".