Outcomes of minor amputations in patients with peripheral vascular disease over a 10-year period at a tertiary care institution
Bibliographic record
Abstract
OBJECTIVES: Choosing an optimal amputation level requires balance between maximizing limb salvage while minimizing chances of non-healing wounds and re-amputation. Our aim was to assess the long-term outcome for minor amputations in patients with peripheral vascular disease. METHODS: A retrospective study of minor amputations between January 1, 2005 and December 31, 2015 was performed. Electronic medical records of eligible patients were examined to extract demographics, co morbidities and clinical data. RESULTS: Within the study period, 220 patients underwent 296 primary minor amputations in 244 lower extremities. Wound healing was achieved in 18.2% (54 of 296 amputations) and 43.6% (129 of 296 amputations) at 90 days and 365 days, respectively. Rates of progression to major amputation were 16.4% (40 or 244 limbs) and 21.7% (53 of 244 limbs) at 90 days and 365 days, respectively. In the final multivariate model, lower ipsilateral posterior tibial waveforms predicted poor 90-day healing (OR = 2.22, p = 0.01) as well as limb loss (OR = 3.02, p = 0.02) in a dose-response manner. In the final logistic regression model, emergency department admission (OR = 0.20, p < 0.01), ipsilateral posterior tibial waveform (OR = 2.63, p < 0.01), and post-operative infection (OR = 0.30, p < 0.01) were predictors of poor healing status at study endpoint. CONCLUSION: This study shows that a majority of foot amputees require ongoing care for non-healing wounds and a proportion necessitate conversion to major amputation. Adequate vascularization is essential to promote and maintain healing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".