The economic burden of inpatient diabetic foot ulcers in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVE: Diabetic foot ulcer, which often leads to lower limb amputation, is a devastating complication of diabetes that is a major burden on patients and the healthcare system. The main objective of this study is to determine the economic burden of diabetic foot ulcer-related care. METHODS: We conducted a multicenter study of all diabetic foot ulcer patients admitted to general internal medicine wards at seven hospitals in the Greater Toronto Area, Canada from 2010 to 2015, using the GEMINI database. We compared the mean costs of care per patient for diabetic foot ulcer-related admissions, admissions for other diabetes-related complications, and admissions for the top five most costly general internal medicine conditions, using the Ontario Case Costing Initiative. Regression models were used to determine adjusted estimates of cost per patient. Propensity-score matched analyses were performed as sensitivity analyses. RESULTS: Our study cohort comprised of 557 diabetic foot ulcer patients; 2939 non-diabetic foot ulcer diabetes patients; and 23,656 patients with the top 5 most costly general internal medicine conditions. Diabetic foot ulcer admissions incurred the highest mean cost per patient ($22,754) when compared to admissions with non-diabetic foot ulcer diabetes ($8,350) and the top five most costly conditions ($10,169). Using adjusted linear regression, diabetic foot ulcer admissions demonstrated a 49.6% greater mean cost of care than non-diabetic foot ulcer-related diabetes admissions (95% CI 1.14-1.58), and a 25.6% greater mean cost than the top five most costly conditions (95% CI 1.17-1.34). Propensity-scored matched analyses confirmed these results. CONCLUSION: Diabetic foot ulcer patients incur significantly higher costs of care when compared to admissions with non-diabetic foot ulcer-related diabetes patients, and the top five most costly general internal medicine conditions.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".