Differences in the Association between Clinically Relevant Classifications of Glycemia Measures and All-Cause and Cardiovascular Disease Mortality Risk
Bibliographic record
Abstract
Aims: To examine all-cause and cardiovascular disease (CVD) mortality risk in individuals categorized as normal, impaired or type 2 diabetic using clinical cutoffs for fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG) and glycated hemoglobin (HbA1c). Methods:The sample included 5,424 adults with undiagnosed diabetes from the Third National Health and Nutrition Examination Survey with public-access mortality data linkage (follow-up=8.5 ± 2.3 years; 685 deaths).The association between the glycemic measures and all-cause and CVD mortality were analyzed with the measures as continuous and categorical variables.FPG, 2hPG and HbA1c were categorized using the American Diabetes Association criteria for normal, impaired and type 2 diabetes.Results: When analyzed as a continuous variable, 2hPG was most strongly associated with both all-cause and CVD mortality.However, after categorizing each measure using clinical cutoffs, impaired and type 2 diabetic levels of FPG and HbA1c, but only type 2 diabetic levels of 2hPG, were signifi cantly associated with all-cause mortality.For CVD mortality, impaired (HR=1.68[1.16-2.44])and type 2 diabetic (HR=1.88[1.11-3.18)]levels of HbA1c were found to be a signifi cant predictor of mortality risk.However, only type 2 diabetic levels of 2hPG and not impaired levels were signifi cantly associated with CVD mortality.FPG was not a signifi cant predictor of CVD mortality.Conclusions: Clinically relevant categories of HbA1c provide more prognostic information for all-cause and CVD mortality risk than 2hPG.Therefore, given the ease and lower cost of measurement, HbA1c should be considered a benefi cial diagnostic and prognostic alternative screening tool in the clinical setting.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".