Age, BMI, and Race Are Less Important Than Random Plasma Glucose in Identifying Risk of Glucose Intolerance
Bibliographic record
Abstract
OBJECTIVE: Age, BMI, and race/ethnicity are used in National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and American Diabetes Association (ADA) guidelines to prompt screening for pre-diabetes and diabetes, but cutoffs have not been evaluated rigorously. RESEARCH DESIGN AND METHODS: Random plasma glucose (RPG) was measured and 75-g oral glucose tolerance tests were performed in 1,139 individuals without known diabetes. Screening performance was assessed by logistic regression and area under the receiver operating characteristic curve (AROC). RESULTS: NIDDK/ADA indicators age >45 years and BMI >25 kg/m(2) provided significant detection of both diabetes and dysglycemia (both AROCs 0.63), but screening was better with continuous-variable models of age, BMI, and race and better still with models of age, BMI, race, sex, and family history (AROC 0.78 and 0.72). However, screening was even better with RPG alone (AROCs 0.81 and 0.72). RPG >125 mg/dl could be used to prompt further evaluation with an OGTT. CONCLUSIONS: Use of age, BMI, and race/ethnicity in guidelines for screening to detect diabetes and pre-diabetes may be less important than evaluation of RPG. RPG should be investigated further as a convenient, inexpensive screen with good predictive utility.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".