Proximal versus total gastrectomy for proximal early gastric cancer
Bibliographic record
Abstract
BACKGROUND: Recently, the incidence of proximal early gastric cancer (EGC) has been rising rapidly. Prevalent surgical methods are proximal gastrectomy (PG) and total gastrectomy (TG); however, which method is superior remains controversial. We conducted a systematic review and meta-analysis of original articles to compare the short- and long-term clinical outcomes of PG with TG for proximal EGC. METHODS: Databases, including PubMed, Embase, Web of Science, and Cochrane Library were searched up to October 2018. The Newcastle-Ottawa scale was utilized to conduct quality assessments, and publication bias was evaluated using Egger test. STATA version 14.0 was used to perform the meta-analysis. RESULTS: A total of 2036 patients with proximal EGC in 18 studies were included in the meta-analysis. The results showed that PG was potentially superior to TG regarding operation time, intraoperative blood loss volume, and long-term nutritional status. Overall survival between the PG and TG groups was not significantly different. PG was associated with a high incidence of 2 kinds of postoperative complications: anastomotic stenosis and reflux esophagitis. However, the incidence of these complications associated with esophagojejunostomy with double-tract reconstruction (DTR) was comparable with that of TG. CONCLUSIONS: PG has several advantages over TG for the treatment of proximal EGC, including surgical outcomes and long-term nutritional status. However, anastomotic stenosis and reflux esophagitis frequently occurred in patients undergoing PG. Esophagojejunostomy with DTR could offer a solution to reducing the incidence of these complications.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".