Pedicled Functional Latissimus Flaps for Reconstruction of the Upper Extremity following Resection of Soft-Tissue Sarcomas
Bibliographic record
Abstract
(1) Background: Resection of soft-tissue sarcomas (STS) of the upper extremity can result in substantial functional impairment with limited options for functional reconstruction. Free functional latissimus flaps have been utilized to restore function of the thigh; however, there is limited data on the use of latissimus flaps for functional reconstruction in the upper extremity. As such, we sought to evaluate our institutional experience with these flaps. (2) Methods: We reviewed ten (seven male; three female; and a mean age of 63 years) patients undergoing soft-tissue sarcoma resection involving the triceps (n = 4), biceps (n = 4), and deltoid (n = 2) reconstructed with a pedicled functional latissimus flap. All surviving patients had at least 1 year of follow-up, with a mean follow-up of 5 years. (3) Results: The mean elbow range of motion and shoulder elevation were 105° and 150°. The mean Musculoskeletal Tumor Society score was 88%, and the muscle strength was four. Four patients had a recipient site wound complication. There were no flap losses. One patient sustained a radiation-associated humerus fracture 5 years postoperatively, treated nonoperatively. (4) Conclusions: Although early complications are high, pedicled functional latissimus flaps allow for wound coverage, potential space obliteration, and restoration of function in the upper extremity following resection of large soft tissue sarcomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".