Sex Differences in All-Cause and Cardiovascular Mortality, Hospitalization for Individuals With and Without Diabetes, and Patients With Diabetes Diagnosed Early and Late
Bibliographic record
Abstract
OBJECTIVE: To compare risk of all-cause mortality, cardiovascular disease (CVD) mortality, acute myocardial infarction (AMI) mortality, stroke mortality, and hospitalizations for males and females with and without diabetes and those with diabetes diagnosed early and late. RESEARCH DESIGN AND METHODS: We conducted a population-based retrospective cohort study including 73,783 individuals aged 25 years or older in Newfoundland and Labrador, Canada (15,152 with diabetes; 9,517 with late diagnoses). RESULTS: Males and females with diabetes had an increased risk of all-cause mortality, CVD mortality, AMI mortality, and CVD hospitalizations compared with individuals without diabetes, and the risk was stronger in females than in males. For females, risks of all-cause mortality (hazard ratio [HR] 1.85 [95% CI 1.74-1.96]) and CVD hospitalizations (2.57 [2.24-2.94]) were significantly higher compared with their male counterparts (1.59 [1.51-1.69] and 1.92 [1.72-2.14]). Females with diabetes diagnosed late had an increased risk of CVD mortality (6.54 [4.80-8.91]) and CVD hospitalizations (5.22 [4.31-6.33]) compared with females without diabetes, and both were significantly higher compared with their male counterparts (3.44 [2.47-4.79]) and (3.33 [2.80-3.95]). CONCLUSIONS: Females with diabetes have a greater risk of mortality than males with diabetes. CVD has a greater impact on females with diabetes than males, especially when diagnosed at a later stage. Different management strategies should be considered for males and females and those with early and late diagnoses of diabetes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".