Strategies to detect abnormal glucose metabolism in people at high risk of cardiovascular disease from the ORIGIN (Outcome Reduction with Initial Glargine Intervention) trial population
Bibliographic record
Abstract
BACKGROUND: To investigate whether the combination of HbA1c and fasting plasma glucose (FPG) can be used for the diagnosis of diabetes and impaired glucose tolerance (IGT) in people at high risk of cardiovascular disease (CVD). METHODS: A cross-sectional study was performed on 2907 people at high risk of cardiovascular events but without a previous diagnosis of diabetes. Optimal cut-off points and the diagnostic potential of FPG, HbA1c, and their combination were determined. RESULTS: The sensitivity of the usually applied FPG cut-off point of 7.0 mmol/L to diagnose diabetes mellitus was low (59.0%). Receiver operating characteristic (ROC) curve analysis indicated that the optimal cut-off points for the diagnosis of diabetes using FPG and HbA1c were 6.4 mmol/L (sensitivity 75.7%; specificity 77.5%; likelihood ratio 3.37) and 5.9% (41 mmol/mol; sensitivity 68.7%; specificity 67.1%; likelihood ratio 2.09), respectively. To diagnose IGT, the optimal cut-off points for FPG and HbA1c were 6.1 mmol/L (sensitivity 57.1%; specificity 57.9%) and 5.7% (39 mmol/mol; sensitivity 63.8%; specificity 60.3%), respectively. For diabetes, combining cut-off points for FPG and HbA1c identified four categories with likelihood ratios ranging from 5.59 to 0.21, and post-test probabilities between 69.3% and 7.8%. For IGT, likelihood ratios varied between 2.05 and 0.56, whereas post-test probabilities ranged from 84.0% to 58.8%. CONCLUSIONS: Using FPG alone results in the underdiagnosis of glucometabolic abnormalities in people at high risk of CVD. Using an algorithm with both HbA1c and FPG improves the detection of diabetes, but not IGT, and could be easily implemented in patient care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".