Shoulder Function after Harvesting a Thoracodorsal Artery Perforator Flap
Bibliographic record
Abstract
BACKGROUND: Converting the latissimus dorsi musculocutaneous flap to a thoracodorsal artery perforator flap has been reported for breast reconstruction. The goal of this article is to evaluate the donor site after harvesting a thoracodorsal artery perforator flap in patients who underwent breast surgery and to show the advantages of sparing the latissimus dorsi muscle on the function of the shoulder. METHODS: Between 2002 and 2004, 22 patients who had a partial breast reconstruction using a pedicled thoracodorsal artery perforator flap were enrolled in a functional study to evaluate shoulder function postoperatively. Latissimus dorsi muscle strength, shoulder mobility, and latissimus dorsi thickness were measured by using the MicroFet2, a goniometer, and ultrasound examination, respectively. The measurements of the operated and contralateral (unoperated) sides were analyzed statistically. RESULTS: When comparing the operated sides to the unoperated sides, latissimus dorsi strength seemed to be maintained after surgery. Shoulder mobility was also similar in all movements, but both active and passive forward elevation and passive abduction were reduced significantly after surgery, and latissimus dorsi thickness was not affected by harvesting the thoracodorsal artery perforator flaps. No seroma formation was found in any of the donor sites. CONCLUSIONS: Donor-site morbidity after harvesting a thoracodorsal artery perforator flap was reduced to a minimum. Therefore, perforator flaps should be considered in reconstruction whenever adequate perforators can be identified and safely dissected.
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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 | 0.000 |
| 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) | 0.001 | 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".