Management algorithm for failed gastric pull up reconstruction of laryngopharyngectomy defects: Case report and review of the literature
Bibliographic record
Abstract
BACKGROUND: Gastric pull up remains a popular reconstructive option for pharyngoesophagectomy defects extending to thoracic inlet. Gastric necrosis is a dreaded complication of gastric pull up reconstruction and few studies report on management of this complication. MEDLINE, EMBASE, and Web of Science™ databases were searched for publications in the last 25 years on gastric pull up reconstruction following pharyngoesophagectomy. The rates of complications related to gastropharyngeal anastomosis were extracted, and methods of managing gastric necrosis were noted. Forty seven case series were identified reporting on the use of gastric pull up for reconstruction of pharyngoesophageal defects. Mortality rate varied from 0 to 33 % with a weighted average of 8.6 %. In 39 % of patients, mortality was either caused or directly related to failure of the gastropharyngeal anastomosis. The reported rate of gastric necrosis ranged from 0 to 24 % resulting in a 28 % mortality. Options for managing gastric necrosis included: temporary cervical diversion, free jejunum flap, colonic interposition, tubed radial forearm flap, deltopectoralis and pectoralis myocutaneous flaps. CASE PRESENTATION: We present the first case of an anterolateral thigh flap rescue of gastric necrosis after gastric pull up reconstruction. The case report is followed by a review of literature on management of gastric pull up failures. CONCLUSION: Based on the extracted information, we propose an algorithm for managing gastric pull up failure following pharyngoesophageal reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".