PS01.173: MANAGEMENT OF INTRATHORACIC AND CERVICAL ANASTOMOTIC LEAKAGE AFTER ESOPHAGECTOMY FOR ESOPHAGEAL CANCER: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Background Anastomotic leakage affects up to 30% of patients after esophagectomy each year and leads to considerable morbidity and mortality. The aim of this study was to determine which treatment for anastomotic leakage after esophagectomy has the best clinical outcome, based on currently available literature. Methods A systematic literature search was performed in Medline, Embase and Web of Science until April 2017. All studies reporting on the treatment of anastomotic leakage following esophagectomy with gastric tube reconstruction for esophageal or cardia cancer were included. The primary outcome parameter was postoperative mortality. Methodological quality was assessed by the Newcastle-Ottawa Quality Assessment Scale. Results Nineteen retrospective cohort studies including 273 patients were identified. Methodological quality of all studies was poor to moderate. Regarding intrathoracic anastomotic leakages, mortality rates in the conservative, endoscopic stent, endoscopic drainage, endoscopic vacuum assisted closure system and surgery treatment group were 14%, 8%, 8%, 0%, and 50%, respectively. Regarding cervical anastomotic leakages, mortality rates in the conservative, endoscopic stent and endoscopic dilatation group were 8%, 29%, and 0%, respectively. Conclusion Due to small cohorts, heterogeneity between studies, and lack of data regarding leakage characteristics, no evidence supporting one treatment for anastomotic leakage after esophagectomy was found. A severity score based on leakage characteristics instead of treatment given is essential for determining the optimal treatment of anastomotic leakage. A prospective registration study could provide answers to issues as which leakage characteristics determine its severity and which treatment options have the best outcomes for a given anastomotic leakage severity. Disclosure All authors have declared no conflicts of interest.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".