Regional health care services and rates of lower extremity amputation related to diabetes and peripheral artery disease: an ecological study
Bibliographic record
Abstract
BACKGROUND: The care necessary to prevent amputation from diabetes and peripheral artery disease (PAD) remains disjointed in many jurisdictions. To help inform integrated regional care, this study explores the correlation between regional health care services and rates of lower extremity amputation. METHODS: This ecological study included 14 administrative health regions in Ontario, Canada. All diabetes- or PAD-related major (above ankle) amputations (Apr. 1, 2007, to Mar. 31, 2017) were identified among residents 40 years of age and older. For each region, age-and sex-adjusted amputation rates were calculated as well as per capita counts of key health providers (podiatrists and chiropodists, as well as surgeons) and health care utilization among study patients in the year before the first major amputation (physician visits, publicly funded podiatry visits, emergency department visits, hospital admissions, home care nursing, minor amputation, limb revascularization). RESULTS: A total of 11 658 patients with major amputation were identified (of whom 79.2% had diabetes and 96.5% had PAD). There was wide regional variation in amputation rates: 2.53 to 11.77 per 100 000 person-quarters. At a regional level, the proportion of study patients who received revascularization showed the strongest negative correlation with amputation rates. The regional proportion of study patients who saw a vascular surgeon showed the strongest negative correlation with amputation rates, relative to other health provider visits. Other measures of health care utilization among patients correlated poorly with regional amputation rates, as did the regional provider counts. The results were similar when we restricted the analysis to diabetes-related amputations. INTERPRETATION: Amputation rates related to diabetes and PAD vary widely across Ontario. Access to vascular assessment and revascularization must be integrated into regional amputation prevention efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".