Role of the Pectoralis Major Muscle Flap in the Multidisciplinary Treatment of Esophageal Cancer
Bibliographic record
Abstract
Background: Management of esophageal cancer is complex. Esophagectomy is associated with risk of significant complications. In this case series, we share the experience of our multidisciplinary team of thoracic surgeons and otolaryngologists in managing complications arising in the surgical treatment of esophageal cancer with the assistance of regional tissue transfer in the form of the pectoralis major flap. Methods: We present a case series highlighting 3 patients who underwent esophagectomy who experienced significant anastomotic or conduit complications which were managed with a pectoralis muscle flap. Results: Complications included tracheoesophageal fistula, refractory stenosis, and gastric conduit necrosis. Using a pectoralis major muscle flap with both myocutaneous and myofascial transfers was key to successful management. In the first patient, esophageal stent erosion after posterior tracheal wall dissection resulted in a tracheoesophageal fistula reconstructed through interposition of a myofascial flap. In the second patient, a tubed myocutaneous flap was interposed between the remnant gastric conduit and cervical esophagus to manage a posttreatment stenosis following resection of the stenosed segment. Finally, a myofascial flap was utilized to bolster a colonic interposition flap after initial necrosis of a gastric conduit that necessitated the creation of a temporary pharyngocutaneous fistula and subsequent colon interposition. Conclusions: Multidisciplinary care and collaboration are integral components for optimization of patient outcomes. In this case series, otolaryngology and thoracic surgery utilized multiple tools within their armamentarium to manage complications associated with the surgical management of esophageal cancer.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".