Laparoscopic ischaemic conditioning of the gastric conduit prior to a hybrid mckeown oesophagectomy may not decrease the risk of anastomotic leak.
Bibliographic record
Abstract
INTRODUCTION: Morbidity associated with anastomotic leak after oesophagectomy is significant. Techniques to reduce this risk include ischaemic conditioning of the gastric conduit prior to oesophagectomy. AIM: To quantify the rate of anastomotic leak after a hybrid minimally invasive McKeown oesophagectomy preceded by laparoscopic gastric devascularization (LGD). MATERIAL AND METHODS: We identified patients who had undergone neoadjuvant chemoradiotherapy followed by LGD and McKeown oesophagectomy and conducted a retrospective case series. The primary outcome was anastomotic leak, and secondary outcomes included common post-operative complications within 30 days. RESULTS: Eleven patients were identified. Seventy-three per cent were male, and 7 of 11 patients were age 70+ years. 91% of tumours were located in the lower oesophagus or gastroesophageal junction (GEJ), and 72% of the series had clinical stage of II-III. The median ischaemic conditioning time was 15 days. Eighteen per cent of patients developed an anastomotic leak, and all were managed non-operatively. One patient developed an anastomotic stricture. Three patients developed pneumonia. Three patients suffered wound infection at the site of the neck incision. One had respiratory failure requiring ventilator support. None required reoperation or readmission. There were no mortalities following either operation. CONCLUSIONS: Laparoscopic ischaemic conditioning via LGD prior to a hybrid McKeown oesophagectomy for malignancy was associated with a leak rate similar to previously published data for a McKeown oesophagectomy without prior LGD.
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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 | 0.000 | 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) | 0.002 | 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".