Temporal Trends in the Rates of Foot Complications and Lower Extremity Amputation Related to Type 1 and Type 2 Diabetes in Adults in Selected Canadian Provinces
Bibliographic record
Abstract
OBJECTIVE: Our aim in this study was to identify the contemporary annual incidence rates of hospitalization for diabetes-related foot complication (DFC) and lower extremity amputation in Canada. METHODS: The time frame of this study was April 2011 to March 2022. The population included people at least 20 years of age throughout Canada, except for Québec. From the Canadian Institute for Health Information Discharge Abstract Database of acute care hospitalizations, a person's 1) first DFC, 2) first diabetes-related major amputation, and 3) first diabetes-related major or minor amputation were identified. Using population data from Statistics Canada, age- and sex-adjusted annual rates were calculated for each of these events. Regression models for temporal trends in these rates were fitted for the full population and by province or territory. RESULTS: Over the 11-year study period, there were 20,886 first major amputations, 41,643 first major or minor amputations, and 48,526 first DFCs. The average incidence rates across years for major amputation, major or minor amputation, and DFC were 8.8, 17.5, and 20.3 per 100,000 population, respectively. The major amputation rate decreased over time (-0.06 per year [95% confidence interval -0.11 to -0.01]), but there was no change over time for other events. A declining rate for major amputations was observed in Ontario, Manitoba, and Saskatchewan, but not in the other provinces or territories. CONCLUSIONS: The national rate of major amputation related to diabetes has decreased, but the burden of DFCs requiring hospitalization has not. These contemporary data support the need to strengthen foot screening and limb preservation efforts for people living with diabetes across Canada.
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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 | 0.002 | 0.004 |
| 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".