GERD outcome after bariatric surgery
Bibliographic record
Abstract
BACKGROUND/AIM: Obesity is associated with increased incidence of gastroesophageal reflux disease (GERD), and it has been suggested that GERD symptoms may be improved by weight reduction. However, various patterns of bariatric surgery may affect symptoms of GERD due to the changed anatomy of stomach and esophagus. The aim of this systematic review and meta-analysis is to analyze the effect of bariatric surgery on GERD. MATERIALS AND METHODS: A systematic literature search was performed using PubMed, EMBASE, and the Cochrane Library from January 2005 to January 2019, combining the words obesity, gastroesophageal reflux with different types of bariatric surgery and weight loss. The methodological quality of randomized controlled trials and non-randomized controlled trials published in English and have at least 1-year follow-up data were included and assessed by Cochrane Collaboration's tool for assessing risk bias and Newcastle-Ottawa scale. Only clinical trials were included, and case series or case reports were excluded. RESULTS: We anticipate that our review will provide the exact estimates of the burden and phenotype of GERD among patients that have undergone bariatric surgery. CONCLUSION: GERD may improve in obese patients who underwent laparoscopic sleeve gastrectomy (LSG); however, the most favorable effect is likely to be found after Roux-en-Y gastric bypass surgery. PROSPERO REGISTRATION NUMBER: CRD42018090074.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".