Effectiveness of a Multidisciplinary Limb Preservation Program in Reducing Regional Hospitalization Rates for Patients With Diabetes-Related Foot Complications
Bibliographic record
Abstract
Objective This study evaluated the toe and flow model (TFM), a limb preservation program led by podiatric surgeons in Alberta, Canada, for its impact on hospitalization rates and length of stay (LOS) in patients with diabetic foot complication (DFC). Diabetes, a leading cause of non-traumatic lower extremity amputations (LEAs) in Canada, often results in diabetic foot ulcers (DFUs), a major cause of infection, amputation, and hospitalization. TFM has reportedly reduced amputation rates by 39% to 56%. Methods The study analyzed Alberta's health database from 2007 to 2017, focusing on diabetes patients aged 20 and above. It included patients with various DFCs and compared outcomes in regions using TFM and standard of care (SOC). The study also examined data from two major cities, one with TFM and the other without, including rural referrals to Calgary and Edmonton. The data were normalized for the diabetic population and analyzed using a standard Student’s t -test. Results TFM regions showed significantly lower hospitalization rates ( p = 1.22E-12) than SOC regions. Over 11 years, TFM maintained lower average and median LOS by 0.13 and 0.26 days, respectively. TFM access reduced hospitalization risk by up to 30%, and patients in TFM regions had a 21% shorter LOS compared to SOC regions. Conclusion Despite similar demographics and healthcare systems, the TFM region benefited from a dedicated multidisciplinary program and comprehensive limb preservation services. The study shows that TFM effectively reduces hospitalizations and LOS for DFCs, with significantly better outcomes in the TFM region than in SOC regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".