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Nutrition and Stroke Prevention

2006· review· en· W2071598740 on OpenAlexaff
Marc Fisher, Kennedy R. Lees, J. David Spence

Bibliographic record

VenueStroke · 2006
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsMedicineStroke (engine)Intensive care medicine

Abstract

fetched live from OpenAlex

Nutrition is much more important in prevention of stroke than is appreciated by most physicians. The powerful effects of statin drugs in lowering the levels of fasting cholesterol, combined with an unbalanced focus on fasting lipids (as opposed to postprandial fat and oxidative stress), have led many physicians and patients to believe that diet is relatively unimportant. Because the statins can lower fasting lipids by &50% to 60%, and a low-fat diet only lowers fasting cholesterol by &5% to 10%, this error is perhaps understandable. However, a Cretan Mediterranean diet, which is high in beneficial oils, whole grains, fruits, and vegetables and low in cholesterol and animal fat, has been shown to reduce stroke and myocardial infarction by 60% in 4 years compared with the American Heart Association diet. This effect is twice that of simvastatin in the Scandinavian Simvastatin Survival Study: a reduction of myocardial infarction by 40% in 6 years. Vitamins for lowering of homocysteine may yet be shown to be beneficial for reduction of stroke; a key issue is the high prevalence of unrecognized deficiency of vitamin B(12), requiring higher doses of vitamin B(12) than have been used in clinical trials to date. Efforts to duplicate with supplementation the evidence of benefit for vitamins E, C, and beta carotene have been largely fruitless. This may be related to the broad combination of antioxidants included in a healthy diet. A Cretan Mediterranean diet is probably more effective because it provides a wide range of antioxidants from fruits and vegetables of all colors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.353
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations104
Published2006
Admission routes1
Has abstractyes

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