A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT)
Bibliographic record
Abstract
BACKGROUND: National estimates of the upcoming diabetes epidemic are needed to understand the distribution of diabetes risk in the population and to inform health policy. OBJECTIVE: To create and validate a population-based risk prediction tool for incident diabetes using commonly collected national survey data. METHODS: With the use of a cohort design that links baseline risk factors to a validated population-based diabetes registry, a model (Diabetes Population Risk Tool (DPoRT)) was developed to predict 9-year risk for diabetes. The probability of developing diabetes was modelled using sex-specific Weibull survival functions for people > 20 years of age without diabetes (N=19,861). The model was validated in two external cohorts in Ontario (N=26,465) and Manitoba (N=9899). Predictive accuracy and model performance were assessed by comparing observed diabetes rates with predicted estimates. Discrimination and calibration were measured using a C statistic and Hosmer-Lemeshow χ² statistic (χ²(H-L)). RESULTS: Predictive factors included were body mass index, age, ethnicity, hypertension, immigrant status, smoking, education status and heart disease. DPoRT showed good discrimination (C=0.77-0.80) and calibration (χ²(H-L) < 20) in both external validation cohorts. CONCLUSIONS: This algorithm can be used to estimate diabetes incidence and quantify the effect of interventions using routinely collected survey data.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".