Pre-Diabetes Detection and Intervention for High Risk Communities
Bibliographic record
Abstract
This article briefly summarizes the “Pre-Diabetes Detection and Intervention Symposium” that described ongoing and past pre-diabetes interventions, and outlined some considerations when deciding to target specific populations with pre-diabetes. The success of type 2 diabetes (T2D) prevention clinical trials provides clear evidence that healthy lifestyle change can prevent the development of T2D in a cost effective manner in high risk individuals. However, who to target and what cut-points should be used to identify individuals who would qualify for these T2D prevention programs are not simple questions. More stringent cut-offs are more efficient in preventing T2D, but less equitable. Interventions will likely need to be adapted and made more economical for local communities and health care centers if they are to be adopted universally. Further, they may need to be adapted to meet the specific needs of certain high-risk populations such as ethnic minorities. The Chronic Disease Management & Prevention Program for Diverse Populations in Alberta and the Pre-diabetes Detection and Physical Activity Intervention Delivery project in Toronto represent 2 examples of specialized interventions that are targeted at certain high risk populations. To reverse the current T2D trends will require continued efforts to develop and refine T2D prevention interventions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".