Validation of a novel reconstruction method of laparoscopic gastrectomy for proximal early gastric cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recently, a novel surgical procedure, named as laparoscopic proximal gastrectomy (LPG) with double-tract reconstruction (DTR), has been reported to provide surgical benefits in the treatment of proximal early gastric cancer (EGC) over traditional laparoscopic total gastrectomy (LTG). These benefits include a lower incidence of some surgical complications and better postoperative nutritional status. However, the number of relevant studies is still too low to validate such benefits. Therefore, this systematic review and meta-analysis aimed to assess the surgical features, complications, and postoperative nutritional status of LPG with DTR in comparison to those of LTG. METHODS: Online databases (PubMed, Web of Science, Cochrane Library, and EMBASE) were scoured for relevant studies published by April 2020. The quality assessment of the included articles was evaluated using the Newcastle-Ottawa scale. Egger's test was utilized to assess publication bias. RESULTS: Nine studies (687 patients) were enrolled for this meta-analysis, and we found that LPG with DTR and LTG had similar surgical features. However, LPG with DTR was superior to LTG in the incidence of reflux syndrome [OR = 0.185; 95%CI 0.083, 0.414; P = 0.000], postoperative nutritional status (hemoglobin [WMD = - 2.326; 95%CI - 4.491, - 0.160; P = 0.035], vitamin B12 [WMD = - 13.072; 95%CI - 22.850, - 3.294; P = 0.009], and body weight [WMD = - 3.514; 95%CI - 5.579, - 1.449; P = 0.001]). CONCLUSIONS: LPG with DTR has better performance in the incidence of reflux syndrome and postoperative nutritional status compared with LTG. This surgical procedure may therefore have more benefits for patients with proximal EGC.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.048 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".