Relationship between blood glucose and carotid intima media thickness: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Increased coronary intima media thickness (CIMT) has been associated with adverse cardiovascular outcomes, as have increased glucose levels. The link has not been established between glucose and CIMT; therefore, we sought to assess the relationship between glucose and CIMT. METHODS: Medline, EMBASE, Scopus, and Cochrane databases were searched from inception through 2009 for original research reporting both postprandial glucose levels and CIMT measurements. Glucose was classified as normal, impaired, or diabetic. Outputs included inverse variance weighted effect size and also average correlation (using the Wang and Bushman approach). Data were combined using a random effects meta-analytic model. Heterogeneity as assessed using chi(2) and I(2); bias was examined using Egger plots and Begg-Mazumdar tau. Polynomial functions (i.e., linear, quadratic, cubic, quartic) were fit to the data and the Akaike Information Criteria were used to select the optimal model. RESULTS: We identified 172 papers; 161 were rejected (19 inappropriate design, 8 had selected patients, 101 inappropriate outcomes) leaving 11 accepted. We used data from 15,592 patients (8250 normals, 3013 impaired glucose, 4329 diabetics). There was no evidence of heterogeneity or publication bias. The overall correlation was 0.082 (CI95%:0.066-0.098); the overall effect size was 0.294 (0.245-0.343) between diabetics and normals and 0.137 (0.072-0.202) between normals and those with impaired glucose. The equation of best fit was linear (CIMT = 0.828 + 0.009*glucose). CONCLUSIONS: There is a small but significant relationship between postprandial glucose levels and CIMT, which have both been associated with adverse cardiovascular outcomes.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.054 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".