Complex limb salvage with an orthoplastic approach
Bibliographic record
Abstract
Abstract Lower extremity trauma is an important cause of patient morbidity and disability-adjusted life years (DALYs). It also presents a significant reconstructive challenge for surgeons. Mangled extremity injuries can result in an amputation when limb salvage procedures are unsuccessful. Identifying strategies to optimize limb salvage attempts is necessary to improve postoperative outcomes. Establishing a constructive relationship between orthopaedic and plastic surgeons has been shown to be beneficial and subject to a growing interest in the trauma literature. We present the case of a 42-year-old male referred to our institution for limb salvage after sustaining severe bilateral lower extremity injuries. These included a right open Gustilo 3B tibia shaft fracture with a critical bone defect combined with a left floating knee (closed femur fracture and open Gustilo 3C tibia fracture). Bilateral tibia fracture-related infections were treated in conjunction with successful flap coverage. Following eradication of the infections, adequate soft tissue coverage and stable bony fixation, the tibia critical bone defect of close to 8 cm was treated with bone transport. Limb salvage was successful; amputation was avoided, and good function of both limbs was achieved. This manuscript shares a great clinical success in the extensive collaboration between two surgical subspecialties. Highlights
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".