Hand-sewn versus stapled anastomoses for esophagectomy: We will probably never know which is better
Bibliographic record
Abstract
Objective: Esophagectomy remains the mainstay of treatment for nonmetastatic esophageal cancer. The optimal technique for anastomosis after esophagectomy remains unknown. The purpose of this systematic meta-analysis is to combine the available high-quality evidence to provide esophageal surgeons with an evidence base for their decision making. Methods: A systematic search of multiple databases was conducted to find randomized controlled trials of esophageal anastomotic techniques. A meta-analysis of the pooled data was conducted. Results: A total of 19 studies with 2123 patients were included in the meta-analysis. The pooled analysis revealed a 102% higher incidence of anastomotic leak after hand-sewn anastomosis compared with stapled anastomosis (odds ratio [OR], 2.02; 95% confidence interval [CI], 1.48-2.75). Anastomotic stricture rate was also 31% higher with hand-sewn anastomosis (OR, 1.31; 95% CI, 1.00-1.7). Thirty-day mortality did not show statistical difference favoring one anastomosis technique to another (OR, 0.68; 95% CI, 0.45-1.04). None of anastomotic leak rate, anastomotic stricture rate, or 30-day overall survival differed between anastomotic techniques in studies with only thoracic anastomoses. In cervical position hand-sewn anastomosis was associated with higher rate of anastomotic leak (OR, 2.02; 95% CI, 1.33-3.05) and stricture (OR, 1.77; 95% CI, 1.15-2.72), but no difference in 30-day mortality. Conclusions: This meta-analysis showed a signal of higher rate of leak and stricture in hand-sewn anastomoses, but sensitivity analyses did not show a consistent outcome, so these results should be interpreted with caution.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".