Age modification of diabetes-related hospitalization among First Nations adults in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: We sought to determine the modifying effects of age and multimorbidity on the association between First Nations status and hospitalizations for diabetes-specific ambulatory care sensitive conditions (ACSC). FINDINGS: We identified 183,654 adults with diabetes from Alberta Canada, and followed them for one year for the outcome of hospitalization or emergency department (ED) visit for a diabetes-specific ACSC. We used logistic regression to determine the association between First Nations status and the outcome, assessing for effect modification by age and multimorbidity with interaction terms. In a model adjusting for age, age(2), baseline A1c, duration of diabetes, and multimorbidity, First Nations people were at greater risk than non-First Nations to experience a diabetes-specific hospitalization or ED visit (unadjusted odds ratio [OR] 3.74; 95% confidence interval [CI]: 3.45-4.07). After adjustment for relevant covariates, this association varied by age (interaction: p = 0.018): adjusted OR 3.94 (95% CI: 3.11-4.99) and 5.74 (95% CI: 3.36-9.80) for First Nations compared to non-First Nations at ages 30 and 80 years, respectively. CONCLUSIONS: Compared with non-First Nations, older First Nations patients with diabetes are at greater risk for diabetes-specific hospitalizations. Older First Nations patients with diabetes should be given priority access to primary care services as they are at greatest risk for requiring hospitalization for stabilization of their condition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".