The relationship of insulin resistance measured by reliable indexes to coronary artery disease risk factors and outcomes--a systematic review.
Bibliographic record
Abstract
OBJECTIVES: To provide a qualitative and quantitative review of the published literature that examines the relationship between reliable markers of insulin resistance and coronary artery disease risk factors (plasma glucose, triglycerides, high density lipoprotein cholesterol, hypertension, obesity, low density lipoprotein size) and outcomes (as related to ischemic heart disease) in populations with and without type 2 diabetes. METHODS: The MEDLINE database was searched (January 1966 to April 2000). Additional references were identified from bibliographies of retrieved articles. The quantitative relationship between insulin resistance and coronary artery disease outcomes was estimated in individual studies by derivation of 2x2 tables. RESULTS: Of 780 publications reviewed, 28 met the inclusion criteria. Twenty publications investigated the relationship of insulin resistance markers with coronary artery disease risk factor profiles only, while eight publications primarily evaluated coronary artery disease outcomes. CONCLUSIONS: The present review suggests that populations with lower insulin resistance measured by reliable indexes are consistently associated with better overall cardiovascular risk profiles (including reduced clustering of risk factors), and improved coronary artery disease outcomes than populations with elevated insulin resistance.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".