Evidence of Lifestyle Modification in the Management of Hypercholesterolemia
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease (CHD) is the leading cause of morbidity and mortality worldwide. The growth of ageing populations in developing countries with progressively urbanized lifestyles are major contributors. The key risk factors for CHD such as hypercholesterolemia, diabetes mellitus, and obesity are likely to increase in the future. These risk factors are modifiable through lifestyle. OBJECTIVES: To review current literature on the potential benefit of cholesterol lowering in CHD risk reduction with a particular focus on the evidence of non-pharmacological/lifestyle management of hypercholesterolemia. METHODS: Medline/PubMed systematic search was conducted using a two-tier approach limited to all recent English language papers. Primary search was conducted using key words and phrases and all abstracts were subsequently screened and relevant papers were selected. The next tier of searching was conducted by (1) reviewing the citation lists of the selected papers and (2) by using PubMed weblink for related papers. Over 3600 reports were reviewed. RESULTS: Target cholesterol levels set out in various guidelines could be achieved by lifestyle changes, including diet, weight reduction, and increased physical activity with the goal of reducing total cholesterol to <200 mg/dL and LDL-C<100 mg/dL. Various dietary constituents such as green tea, plant sterols, soy protein have important influences on total cholesterol. Medical intervention should be reserved for those patients who have not reached this goal after 3 months of non-pharmacological approach. CONCLUSION: CHD remains as a leading cause of death worldwide and hypercholesterolemia is an important cause of CHD. Non-pharmacological methods provide initial as well as long-term measures to address this issue.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".