Free Tissue Transfer after Open Transmetatarsal Amputation in Diabetic Patients
Bibliographic record
Abstract
BACKGROUND: Transmetatarsal amputation (TMA) preserves functional gait while avoiding the need for prosthesis. However, when primary closure is not possible after amputation, higher level amputation is recommended. We hypothesize that reconstruction of the amputation stump using free tissue transfer when closure is not possible can achieve similar benefits as primarily closed TMAs. METHODS: Twenty-eight TMAs with free flap reconstruction were retrospectively reviewed in 27 diabetic patients with a median age of 61.5 years from 2004 to 2018. The primary outcome was limb salvage rate, with additional evaluation of flap survival, ambulatory status, time until ambulation, and further amputation rate. In addition, subgroup analysis was performed based on the microanastomosis type. RESULTS: Flap survival was 93% (26 of 28 flaps) and limb salvage rate of 93% (25 of 27 limbs) was achieved. One patient underwent a second free flap reconstruction. In the two failed cases, higher level amputation was required. Thirteen flaps had partial loss or other complications which were salvaged with secondary intension or skin grafts. Median time until ambulation was 14 days following reconstruction (range: 9-20 days). Patients were followed-up for a median of 344 days (range: 142-594 days). Also, 88% of patients reported good ambulatory function, with a median ambulation score of 4 out of 5 at follow-up. There was no significant difference between the subgroups based on the microanastomosis type. CONCLUSION: TMA with free flap reconstruction is an effective method for diabetic limb salvage, yielding good functional outcomes and healing results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".