Changes in gastric mucosal microbiota in gastric carcinogenesis: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The human stomach is a complex and diverse microbial ecosystem. Consecutive alternations of gastric microbiota occur in gastric carcinogenesis, while the changing pattern during this process remains controversial across studies. We aim to identify the changes in the diversity and composition of gastric mucosal microbiota in gastric tumorigenesis. METHODS AND ANALYSIS: We will search through PubMed, EMBASE and Cochrane databases, as well as conference proceedings and references of review articles for observational articles reporting either the relative abundance of bacteria at the phylum or genus level or at least one of the alpha diversity indexes respectively and clearly in both gastric cancer and non-cancer groups. Selection of studies and data extraction will be performed independently by two researchers. Disagreements will be resolved through discussion. Risk of bias will be assessed using the modified Newcastle-Ottawa Scale. Quantitative analyses will be performed using a random effects model, where the effect measurement will be expressed as the MD. ETHICS AND DISSEMINATION: Ethical approval for this systematic review is not required, as the study is based exclusively on published documents and will not include any individual data. Findings of this study are expected to be disseminated through peer-reviewed journals or conference proceedings. PROSPERO REGISTRATION NUMBER: CRD42020206973.
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 | 0.068 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.013 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.008 |
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".