Nova Scotia Prediabetes Project: upstream screening and community intervention for prediabetes and undiagnosed type 2 diabetes
Bibliographic record
Abstract
INTRODUCTION: Identifying individuals in the prediabetic state may help delay/prevent disease progression to type 2 diabetes mellitus. We explored the feasibility of a household mailing approach for population-based screening of prediabetes and unidentified type 2 diabetes mellitus, developed standard protocol, and developed and implemented community-based lifestyle programs. METHODS: The 16-item Canadian Diabetes Risk Assessment Questionnaire (CANRISK) was mailed to every household in two rural Nova Scotia communities. In total 417 participants aged 40 to 74 years with no prior diagnosis of diabetes self-administered the CANRISK and completed a 2-hour oral glucose tolerance test (OGTT) at a local health care facility. Those with prediabetes were invited to participate in a Prediabetes Lifestyle Program. RESULTS: Glycemic status was identified as normal, prediabetes or diabetes for 84%, 13% and 3% of participants, respectively. Association between glycemic status and overall CANRISK risk score was statistically significant. Six CANRISK items were significantly associated with glycemic status: body mass index, waist circumference, history of hypertension and hyperglycemia, education and perceived health status. Participants and physicians gave positive feedback on the CANRISK screening process. CONCLUSION: The CANRISK holds promise as a population-based screening tool.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".