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Dietary cholesterol and other nutritional considerations in people with diabetes

2009· review· en· W2161018252 on OpenAlexaff
David C.W. Lau

Bibliographic record

VenueInternational Journal of Clinical Practice · 2009
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineDiabetes mellitusCholesterolDietary CholesterolLdl cholesterolGerontologyEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Nutrition therapy is an integral component of lifestyle intervention and self-management of people with diabetes. The goals of nutrition therapy are to optimise or maintain quality of life, physiological and mental health, and to prevent and treat acute and long-term complications of diabetes, the associated comorbid conditions and concomitant disorders. Monitoring dietary cholesterol consumption and salt intake are important nutritional aspects to lower the risk for and treatment of cardiovascular disease and hypertension. AIMS: To evaluate the role of nutritional therapy and notably the effect of egg consumption on cardiovascular disease (CVD) risk in people with diabetes. METHODOLOGY: Literature review of nutritional therapy and clinical studies on egg consumption and CVD risk for people with diabetes were conducted and appraised. RESULTS: The Harvard Egg Study on two large prospective US cohorts found that eating one or more eggs a day had no adverse effects on lipid profile or cardiovascular disease risk in men or women. Similar findings were observed in the NHANES-I and Physicians' Health Study. The only exception was people with diabetes, where CVD was increased with eating more than one egg per day. CONCLUSIONS: Consumption of one or more eggs per day is associated with an elevated risk of coronary heart disease in people with diabetes. The mechanism for this association remains unknown but should be explored in randomised clinical trials.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.144
GPT teacher head0.495
Teacher spread0.351 · 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 designOther design
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

Citations4
Published2009
Admission routes1
Has abstractyes

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