Peripheral Arterial Disease Among First Nations People with Diabetes in Ontario, Canada: Linkage of Population-Level Healthcare Data
Bibliographic record
Abstract
IntroductionIndigenous people worldwide are overrepresented and adversely effected by diabetes. Peripheral arterial disease and amputation are among the most feared complications of diabetes, leading to profound impacts on patients’ quality of life. This study linked population-level healthcare data to assess the risk of peripheral arterial disease among First Nations people in Ontario with diabetes. Objectives and ApproachWe linked individual-level population-based healthcare administrative datasets with the Indian Register. The latter provides information on all registered or Status First Nations people in Canada. We compared First Nations people with diabetes with other people in Ontario with diabetes . Age and sex-adjusted rates peripheral revascularization procedures and lower-extremity amputations were calculated for each 12-month period from April 1, 1995, to March 31, 2015. Mortality among those with amputation was determined. ResultsFirst Nations people received revascularization procedures at a comparable rate to other people in Ontario. However, they had lower-extremity amputations at 3- to 5-times the frequency of other Ontario residents. First Nations people had increased mortality after lower-extremity amputation (adjusted hazard ratio 1.15, 95% confidence interval 1.05 to 1.26), with median survival of only 3.5 years. Conclusion / ImplicationsFirst Nations people with diabetes in Ontario had a comparable rate of revascularization but a markedly increased risk for lower-extremity amputation compared to other people in Ontario. This discordance suggests that peripheral arterial disease may be underdiagnosed or undertreated among First Nations people in Ontario, and demonstrates an important health inequity faced by First Nations people.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".