Treatment of intrathoracic anastomotic leakage following esophagectomy for gastroesophageal cancer: a systematic review
Bibliographic record
Abstract
Anastomotic leakage (AL) is a significant complication following esophagectomy. AL affects 8%-17% of patients and is associated with increased morbidity, mortality, and hospital stay. To this date, no consensus exists on the most optimal treatment. This systematic review aimed to determine the most effective treatment approach. A systematic search of Medline, Web of Science, Cochrane, Scopus, and Embase databases was conducted. Only studies reporting on the treatment of intrathoracic anastomotic leakage after esophagectomy with gastric conduit reconstruction for cancer were included. Studies investigating other esophageal disorders or failing to report the location of the anastomosis were excluded. The methodological quality and risk of bias were assessed using the Newcastle-Ottawa Scale for cohort studies. Out of 12,966 identified studies, 38 were included for analysis after removing duplicates and screening titles, abstracts, and full texts. Of these, five were found to be of poor methodological quality and 33 were of moderate quality. The most researched treatment methods were Endoluminal vacuum therapy (EVT), naso-fistula tube drainage (NFTD), and stent treatment. The success and mortality rates for EVT were 82% and 10.7%, for NFTD, 94% and 5.2%, and, for stent treatment, 75.1% and 13.5%, respectively. AL can be effectively treated with EVT, stent treatment, and NFTD. The NFTD approach appeared to have a higher success rate and lower mortality than other treatment modalities. However, it requires a longer treatment duration. Due to limitations within the included studies, a definitive recommendation regarding the optimal treatment for AL cannot be made.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".