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Record W3023408127 · doi:10.1089/bari.2019.0073

Influence of <i>Helicobacter pylori</i> Infection on Outcomes After Bariatric Surgery: A Systematic Review and Meta-Analysis

2020· review· en· W3023408127 on OpenAlexaboutno aff
Xiaoyang Qi, Yu Gong, Yuwen Jiao, Jie Zhao, Yan Zhou, Xiaohan Cui, Song Li, Haojun Yang, Jun Qian

Bibliographic record

VenueBariatric Surgical Practice and Patient Care · 2020
Typereview
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCochrane LibraryOdds ratioMeta-analysisConfidence intervalHelicobacter pyloriInternal medicineHelicobacter pylori infectionMEDLINESurgeryGastroenterology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.003
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.318
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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