Helicobacter pylori Antibiotic Resistance in Russia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: This systematic review and meta-analysis aims to evaluate the temporal changes in Helicobacter pylori antibiotic resistance in Russia based on studies published over the past 15 years. Materials and Methods: We conducted a comprehensive literature search in MEDLINE/PubMed, EMBASE, the Russian Science Citation Index, and Google Scholar, following the PRISMA 2020 guidelines. Our meta-analysis was pre-registered in PROSPERO (CRD 420251025636). The inclusion criteria included original research, published in English or Russian in 2011–2024, involving antibiotic susceptibility testing in treatment-naive Russian adults using validated diagnostic methods. Two independent researchers selected studies and extracted data using standardized procedures, with methodological quality assessed via the Newcastle–Ottawa Scale. Pooled resistance rates were calculated using fixed/random-effects models in MedCalc 23.1.5 and Python 3.9.21, with meta-regression investigating temporal trends and subgroup analyses examining regional and methodological variations. Results: We identified 16 studies comprising 1206 H. pylori isolates. The pooled analysis of studies (2011–2025) revealed an overall clarithromycin resistance rate of 15.236%, with a significant temporal increase from 11.903% pre-2015 to 21.024% in 2020–2024 (p = 0.0049). Metronidazole showed consistently high pooled resistance (33.309%), while amoxicillin (1.828%), levofloxacin (19.014%), tetracycline (1.328%), and rifampicin (5.803%) maintained low resistance rates, and dual clarithromycin–metronidazole resistance was observed in 2.793% of isolates. Regional disparities were notable in the two largest cities of Russia, with 18.763% clarithromycin resistance in Moscow versus 28.540% in Saint-Petersburg. Conclusions: Russia surpasses the Maastricht VI Consensus resistance threshold for clarithromycin (15%), necessitating revision of empirical treatment strategies. The significant increase in clarithromycin resistance, potentially exacerbated by antibiotic use during the COVID-19 pandemic, underscores the urgent need for resistance-guided therapies and ongoing national surveillance programs to optimize H. pylori management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".