Identifying the Profile of Helicobacter pylori-Negative Gastric Cancers: A Case-Only Analysis within the Stomach Cancer Pooling (StoP) Project
Bibliographic record
Abstract
Background: The prevalence of Helicobacter pylori-negative gastric cancer (HpNGC) can be as low as 1%, when infection is assessed using more sensitive tests or considering the presence of gastric atrophy. HpNGC may share a high-risk profile contributing to the occurrence of cancer in the absence of infection. We estimated the proportion of HpNGC, using different criteria to define infection status, and compared HpNGC and positive cases regarding gastric cancer risk factors. Methods: Cases from 12 studies from the Stomach cancer Pooling (StoP) Project providing data on H. pylori infection status determined by serologic test were included. HpNGC was reclassified as positive (eight studies) when cases presented CagA markers (four studies), gastric atrophy (six studies), or advanced stage at diagnosis (three studies), and were compared with positive cases. A two-stage approach (random-effects models) was used to pool study-specific prevalence and adjusted odds ratios (OR). Results: Among non-cardia cases, the pooled prevalence of HpNGC was 22.4% (n = 166/853) and decreased to 7.0% (n = 55) when considering CagA status; estimates for all criteria were 21.8% (n = 276/1, 325) and 6.6% (n = 97), respectively. HpNGC had a family history of gastric cancer more often [OR = 2.18; 95% confidence interval (CI), 1.03-4.61] and were current smokers (OR = 2.16; 95% CI, 0.52-9.02). Conclusion: This study found a low prevalence of HpNGC, who are more likely to have a family history of gastric cancer in first-degree relatives. Impact: Our results support that H. pylori infection is present in most non-cardia gastric cancers, and suggest that HpNGC may have distinct patterns of exposure to other risk factors.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".