Development and Validation of a Diabetes Risk Prediction Model With Individualized Preventive Intervention Effects
Bibliographic record
Abstract
OBJECTIVE: Type 2 diabetes risk prediction models lack the option to predict risk conditional on initiating different preventive interventions. Our objective was to develop and validate a diabetes risk prediction model with individualized preventive intervention effects among racially diverse populations. METHODS: The derivation cohort included participants in the Diabetes Prevention Program (DPP) trial randomized to placebo, metformin, or intensive lifestyle intervention (n = 2640). A risk prediction model for incident diabetes was developed using Cox proportional hazards regression using clinically available predictors: sex, glycated hemoglobin, fasting plasma glucose (FPG), body mass index (BMI), triglycerides, and intervention. To create individualized intervention effects, pairwise interactions between intervention and age, FPG, and BMI were included. The discrimination, calibration, and net benefit of the model's 3-year predictions for incident diabetes were internally validated within the DPP and externally validated among participants with prediabetes in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 2104). RESULTS: In DPP and MESA, mean (SD) age was 51 years (11) and 64 (10), and 67% and 50% of participants were women, respectively. The mean C-statistic was 0.71 [95% confidence interval (CI): 0.68, 0.74] in DPP and 0.86 (95% CI: 0.83, 0.88) in MESA. The optimal preventive intervention (lowest 3-year risk) was lifestyle for 86% and 97% of DPP and MESA participants, respectively, and metformin for the remaining. Model performance was similar across race/ethnicity groups. CONCLUSION: This is the first study to develop and validate a diabetes risk prediction model with individualized preventive intervention effects that may improve clinical decision-making and diabetes prevention.
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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 | 0.014 | 0.027 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".