<i>cagA</i>-Seropositive Strains of<i>Helicobacter pylori</i>Increase the Risk for Gastric Cancer more than the Presence of<i>H pylori</i>Alone
Bibliographic record
Abstract
Huang et al have performed a meta-analysis to determine the relationship betweencagA seropositivity (by serology and polymerase chain reaction) and the risk of gastric cancer. An extensive review of the literature identified no previous systematic overviews. The authors identified 16 studies involving 2284 cases and 2770 controls. The overall prevalence of Helicobacter pylori was 77.7% in cases and 63.1% in controls. Tests forcagA were positive in 62.8% of cases and 37.5% of controls. Thus,H pyloriandcagA seropositivity significantly increased the risk for gastric cancer, by 2.28 (95% CI 1.71 to 3.05) and 2.87 (95% CI 1.95 to 4.22), respectively. In patients withH pylori, those who were infected by acagA-positive strain had a slightly higher risk of gastric cancer, with an odds ratio of 1.64 (95% CI 1.21 to 2.24). The authors also found that patients infected withH pyloriwith or withoutcagA seropositivity had an increased risk of noncardia gastric cancer, but not of cancer of the gastric cardia. They concluded thatcagA-positive strains confer a greater risk of gastric cancer than does H pylori infection alone.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".