A Comparison of Different Types of Esophageal Reconstructions: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: A total esophagectomy with gastric tube reconstruction is the mainstream procedure for esophageal cancer. Colon interposition and free jejunal flap for esophageal reconstruction are the alternative choices when the gastric tube is not available. However, to date, a solution for the high anastomosis leakage rates among these three types of conduits has not been reported. The aim of this network meta-analysis was to investigate the rate of anastomotic leakage (AL) among the three procedures to determine the best esophageal substitute or the future direction for improving the conventional gastric pull-up (GPU). METHODS: We searched PubMed, Cochrane, and Embase databases. We included esophageal cancer patients receiving esophagectomy and excluded patients with other cancer. The random effect model was used in this network meta-analysis. The Newcastle-Ottawa Scale (NOS) was used for the quality assessment of studies in the network meta-analysis, and funnel plots were used to evaluate publication bias. The primary outcome is anastomosis leakage; the secondary outcomes are stricture formation, length of hospital stays, and mortality rate. RESULTS: Nine studies involving 1613 patients were included in this network meta-analysis. The trend results indicated the following. Regarding anastomosis leakage, free jejunal flap was the better procedure; regarding stricture formation, colon interposition was the better procedure; regarding mortality rate, free jejunal flap was the better procedure; regarding length of hospital stay, gastric pull-up was the better treatment. DISCUSSION: Overall, if technically accessible, free jejunal flap is a better choice than colon interposition when gastric conduit cannot be used, but further study should be conducted to compare groups with equal supercharged patients. In addition, jejunal flap (JF) cannot replace traditional gastric pull-up (GPU) due to technical complexities, more anastomotic sites, and longer operation times. However, the GPU method with the supercharged procedure would be a possible solution to lower postoperative AL. The limitation of this meta-analysis is that the number of articles included was low; we aim to update the result when new data are available. FUNDING: None. REGISTRATION: N/A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".