Factors associated with a diabetes diagnosis and late diabetes diagnosis for males and females
Bibliographic record
Abstract
AIMS: To examine the factors associated with diabetes, a late diabetes diagnosis, and whether these factors are different for males and females. METHODS: Cross-sectional study including 7101 individuals aged ≥25 years in Newfoundland and Labrador, Canada (466 with diabetes; 332 diagnosed late). Logistic regression analysis was used to determine the factors associated with a diabetes diagnosis and late diabetes diagnosis. RESULTS: For males, overweight/obesity (HR, 1.35; 95% CI, 1.06-1.72) was positively associated with diabetes while being a regular/occasional drinker (HR, 0.53; 95% CI, 0.32-0.88) was inversely associated with diabetes. Living in a rural area (HR, 1.47; 95% CI, 1.01-2.15), receiving social assistance (HR, 2.80; 95% CI, 1.52-5.15), having poor self perceived health (HR, 2.06; 95% CI, 1.32-3.21), and considering most days stressful (HR, 1.45; 95% CI, 1.01-2.10) were positively associated with diabetes for females. No factors were significantly associated with a late diabetes diagnosis for males. Having a low education (OR, 0.33; 95% CI, 0.11-0.99) was inversely associated with a late diabetes diagnosis for females. CONCLUSIONS: Different factors are associated with diabetes for males and females. Disadvantaged females appear to be at the greatest risk. The factors associated with a late diabetes diagnosis were also different for males and females. Females with lower education levels are diagnosed with diabetes earlier than females with higher education levels. Certain risk factors appear to impact males and females differently and more research is needed on how males and females develop diabetes and when they are diagnosed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".