The Effect of Surgical Approach on Clinical Outcomes in 535 Patients with Remnant Gastric Cancer
Bibliographic record
Abstract
Purpose: This study aimed to evaluate the effect of laparoscopic gastrectomy (LG) and open gastrectomy (OG) on clinical outcomes in patients with remnant gastric cancer (RGC). Materials and Methods: The databases of PubMed, EMBASE, and Cochrane Library were used to search for eligible studies from inception to April 1st, 2023. Hazard ratios (HRs), mean difference (MD), odds ratios (OR), and 95% confidence intervals (CIs) were pooled up to analyze. The Newcastle-Ottawa Scale (NOS) scores were used to evaluate the quality of the included studies. This study was performed with RevMan 5.3 (The Cochrane Collaboration, London, United Kingdom) software. Results: A total of 11 studies involving 535 RGC patients were included in this study. In terms of basic information, we found that the OG group had a higher American Society of Anesthesiologists (ASA) grade (≥2) (OR = 0.24, I 2 = 54%, 95% CI = 0.08–0.71, P = .01) than the LG group. In terms of postoperative outcomes, we found that the LG group had longer operative time (MD = 33.95, I 2 = 58%, 95% CI = 15.05–52.85, P < .01), shorter postoperative hospital stay (MD = 5.08, I 2 = 84%, 95% CI = −9.74 to −0.42, P = .03), shorter length of incision (MD = −7.15, I 2 = 94%, 95% CI = −10.99 to −3.31, P < .01), earlier food intake (MD = −3.09, I 2 = 76%, 95% CI = −4.84 to −1.35, P < .01), and earlier time to first flatus (MD = −0.84, I 2 = 0%, 95% CI = −1.09 to −0.59, P < .01). We found that there was no statistically significant difference in overall survival (HR = 0.96, I 2 = 0%, 95% CI = 0.48–1.93, P = .92) between the LG group and the OG group. Conclusion: LG for RGC patients had longer surgical time, shorter postoperative hospital stay, shorter length of incision, earlier food intake, and earlier time to first flatus.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| 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".