Impact of <i>Helicobacter pylori</i> on the gastric microbiome in patients with chronic gastritis: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
Introduction Chronic gastritis is a common disease worldwide. Studies have consistently shown that chronic gastritis is usually associated with gastric microbial dysbiosis, especially the infection of Helicobacter pylori . However, the interaction between H. pylori and non- H. pylori bacteria in patients with chronic gastritis has not been clearly identified yet. Consequently, we designed a protocol for a systematic review and meta-analysis, which focused on identifying the changes in gastrointestinal microbiota composition between patients with H. pylori -infective and non-infective chronic gastritis. Method and analysis We will search PubMed, EMBASE and Cochrane Library databases to retrieve observational studies on humans. The eligible studies must include data about the relative abundance of the gastrointestinal microbiome in patients with H. pylori -infective or non-infective chronic gastritis. Only the data of adults aged over 18 years will be analysed. Two researchers will extract the data independently, and Newcastle–Ottawa Scale will be used to assess the risk of bias. Random-effects model will be performed in quantitative analyses. Correlation analysis, bioinformatics analysis and function analysis will be performed. Discussion Currently, numerous studies have revealed the role of H. pylori in chronic gastritis. However, the alterations of non- H. pylori bacteria in patients with chronic gastritis remain an open question. The results of our study might provide new insights into future diagnosis and treatments. Ethics and dissemination This study is based on published documents, unrelated to personal data, so ethical approval is not in need. The results of this study are expected to be published in journals or conference proceedings. PROSPERO registration number CRD42020205260; Pre-results.
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 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 | 0.071 | 0.103 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.027 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".