Impact of obesity on the outcomes after gastrectomy for gastric cancer: A meta-analysis
Bibliographic record
Abstract
The impact of body mass index (BMI) on surgical outcomes has previously been studied in relation to several oncological procedures. Regarding gastric cancer surgery, published results have been contradicting in terms of degree of technical difficulty, risk of postoperative complications and survival. In an attempt to clarify these issues, we performed a meta-analysis to evaluate the impact of obesity (defined as BMI ≥ 30 kg/m2) on outcomes after gastrectomy for gastric cancer. The meta-analysis was performed according to the PRISMA guidelines. Eligible studies were identified through search of PubMed, EMBASE, Web of Science and Cochrane Library databases. Quality assessment was performed using the Newcastle-Ottawa scale. The meta-analysis was conducted using random-effects modeling. A total of 11 studies with 13 538 patients were eligible for analysis. Obesity was associated with a significantly longer operation time (WMD = 19.38 min, 95% CI 12.72–26.04; p < 0.001), increased risk of overall complications (RR = 1.23, 95% CI 1.06–1.42; p = 0.005) and pulmonary complications (RR = 3.81, 95% CI 2.24–6.46; p < 0.001). These findings remained irrespective type of surgery (laparoscopic vs. open) and type of gastrectomy. No differences were found regarding blood loss, number of resected lymph nodes, anastomotic leakage, hospital stay, 30-day mortality and 5-year overall survival. The conclusion of the current meta-analysis is that high BMI in gastric cancer patients is associated with longer operative time and more frequent overall postoperative complications. However, it has no negative impact on survival, indicating that gastrectomy is a safe procedure for this group of patients.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.065 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".