Primary antibiotic resistance in Helicobacter pylori in China: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The incidence of Helicobacter pylori (HP) is 25-50% in developed countries and 80% in developing countries, including 56.2% in China. However, antibiotic resistance of HP is a threat to HP control. The purpose of this study was to comprehensively evaluate primary drug resistance of HP in China. METHODS: The full text of reports of the primary antibiotic resistance prevalence of HP was obtained from multiple databases (PubMed, Web of Science, Evimed, Cochrane Library, and China National Knowledge Internet). Review Manager 5.2 was adopted for meta-analysis, sensitivity analysis, and bias analysis. The Newcastle-Ottawa Scale was used to assess the article quality. RESULTS: In total, 38804 HP samples from 22 trials were extracted. The results suggested that the overall prevalence of amoxicillin, clarithromycin, metronidazole, and levofloxacin resistance among HP in adults was as follows: mean difference (MD) = 1.35%, 95% confidence interval (CI) [1.03%, 1.68%]; MD = 23.76%, 95% CI [20.23%, 27.3%]; MD = 69.32%, 95% CI [64.85%, 73.8%]; and MD = 29.45%, 95% CI [4.90, 176.96], respectively. From the results of sensitivity and publication bias, we find that these results are robust and had little publication bias. CONCLUSION: Our research showed that in China, the prevalence of HP resistance to primary antibiotics warrants attention, especially with regard to metronidazole, levofloxacin, and clarithromycin.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".