Influence of <i>Helicobacter pylori</i> Infection on Outcomes After Bariatric Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background:Helicobacter pylori (HP) infection has an association with postoperative complications after bariatric surgery, but the relationship between them is controversial in the literature. The aim of this meta-analysis is to investigate the influence of HP infection on postoperative outcomes in patients undergoing bariatric surgery. Materials and Methods: We performed a literature search in the PubMed, EMBASE, Web of Science, and the Cochrane Library to identify eligible articles published from inception up to October 1, 2019. Study quality was assessed using the Newcastle-Ottawa scale. Results: Twelve studies involving 257,331 patients were finally included in the study. The summary results showed that there was no significant influence of HP infection on postoperative complications (odds ratio 1.87; 95% confidence interval [CI] 0.69–5.03; p = 0.22). However, sensitivity analysis showed that a decreased percentage excess weight loss (%EWL) at 1 year was detected in HP-positive patients (mean difference −3.41; 95% CI −5.90 to −0.92; p = 0.007). Conclusions: This meta-analysis has demonstrated that no significantly adverse association was found between HP infection with postoperative complications. A decreased %EWL at 1 year after bariatric surgery might be associated with HP infection.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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 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".