Population-based secular trends in lower-extremity amputation for diabetes and peripheral artery disease
Bibliographic record
Abstract
<h3>BACKGROUND:</h3> The evolving clinical burden of limb loss secondary to diabetes and peripheral artery disease remains poorly characterized. We sought to examine secular trends in the rate of lower-extremity amputations related to diabetes, peripheral artery disease or both. <h3>METHODS:</h3> We included all individuals aged 40 years and older who underwent lower-extremity amputations related to diabetes or peripheral artery disease in Ontario, Canada (2005–2016). We identified patients and amputations through deterministic linkage of administrative health databases. Quarterly rates (per 100 000 individuals aged ≥ 40 yr) of any (major or minor) amputation and of major amputations alone were calculated. We used time-series analyses with exponential smoothing models to characterize secular trends and forecast 2 years forward in time. <h3>RESULTS:</h3> A total of 20 062 patients underwent any lower-extremity amputation, of which 12 786 (63.7%) underwent a major (above ankle) amputation. Diabetes was present in 81.8%, peripheral artery disease in 93.8%, and both diabetes and peripheral artery disease in 75.6%. The rate of any amputation initially declined from 9.88 to 8.62 per 100 000 between Q2 of 2005 and Q4 of 2010, but increased again by Q1 of 2016 to 10.0 per 100 000 (<i>p</i> = 0.003). We observed a significant increase in the rate of any amputation among patients with diabetes, peripheral artery disease, and both diabetes and peripheral artery disease. Major amputations did not significantly change among patients with diabetes, peripheral artery disease or both. <h3>INTERPRETATION:</h3> Lower-extremity amputations related to diabetes, peripheral artery disease or both have increased over the last decade. These data support renewed efforts to prevent and decrease the burden of limb loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".