Bibliographic record
Abstract
In 44 years of practicing stroke prevention, I have learned many lessons; in this article, I hope to impart some of them. Three areas of my research are discussed. Controlling resistant hypertension is markedly improved by physiologically individualized therapy based on renin/aldosterone phenotyping; this is particularly important in black patients. Measurement of carotid plaque burden strongly predicts cardiovascular risk and is useful for genetic research and for a process called treating arteries instead of risk factors. Doing so in high-risk patients with asymptomatic carotid stenosis was associated with a >80% reduction in the 2-year risk of stroke and myocardial infarction. It also permitted the identification of extremes of atherosclerosis that are useful for studying both the genetics and the biology of atherosclerosis. Patients with very high plaque burden despite low levels of risk factors have an unexplained phenotype; those with little or no plaque despite high levels of risk factors are protected. Patients with unexplained atherosclerosis have higher plasma levels of toxic metabolites produced by the intestinal microbiome largely from egg yolk, red meat, and protein, and those metabolites are renally excreted. This has important dietary implications for stroke prevention. Lowering of plasma total homocysteine with B vitamins significantly reduces the risk of stroke. That was not apparent in early studies because harm from cyanocobalamin among participants with renal failure obscured the benefit among those with good renal function. We should be using B vitamins to prevent stroke but should use methylcobalamin or oxocobalamin instead of cyanocobalamin.
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) | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".