213-LB: Developing a Prognostic Model to Assess Dysglycemia Risk in Canadians Aged 18-39
Bibliographic record
Abstract
In Canada, the prevalence of diabetes has seen the greatest relative increase in young adulthood, where the disorder is severely pathological compared to later-onset. Still, few prognostic models have been developed to screen young adults for dysglycemia risk and boost early identification and intervention. We sought to establish predictors of dysglycemia risk among young Canadian adults (aged 18-39) and evaluate their utility in identifying high-risk individuals. The Canadian Diabetes Risk Questionnaire (CANRISK) study collected questionnaire, anthropometric, and oral glucose tolerance test (OGTT) data from a large, multiethnic convenience sample of Canadians over two phases. Young adults with diagnosed diabetes, missing OGTT data, or pregnant were excluded. Potential factors that modestly predicted (p<0.20) dysglycemia status (FPG≥6.1mmol/L or 2h-PG≥7.8mmol/L) were entered into a lenient stepwise function, producing a young adult-specific model; risk scores were developed from adjusted odds ratios. Discriminatory ability was assessed by optimism-corrected area under the curve (AUC) via bootstrapping and goodness-of-fit by Hosmer-Lemeshow (H-L) test and calibration plot. More than half of the 3334 participants were female (62.4%), non-white (79.2%), less than 25kg/m2 (50.7%), and reported a family history of diabetes (55.4%); based on OGTT results, 7.3% were dysglycemic. The young adult-specific model displayed an adjusted AUC of 72.9%, and reasonable goodness-of-fit (H-L p=0.49). Model performance was similar when run sex-specifically (males: unadjusted AUC of 72.1%, H-L p=0.67; females: 73.6%, p=0.67). Employing a cut-point of 22, the tool displayed high sensitivity (78.8%) but low specificity (54.0%). Only 3% of those identified as low risk by the tool were misclassified. This young adult-specific risk score shows promise to identify high-risk individuals in a multiethnic Canadian sample. Additional studies are needed to assess its generalizability to new datasets. Disclosure S.A. Srugo: None. Y. Jiang: None. H.I. Morrison: None. M.M. deGroh: None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".