Asia–Pacific consensus guidelines on gastric cancer prevention
Bibliographic record
Abstract
BACKGROUND AND AIM: Gastric cancer is a major health burden in the Asia-Pacific region but consensus on prevention strategies has been lacking. We aimed to critically evaluate strategies for preventing gastric cancer. METHODS: A multidisciplinary group developed consensus statements using a Delphi approach. Relevant data were presented, and the quality of evidence, strength of recommendation, and level of consensus were graded. RESULTS: Helicobacter pylori infection is a necessary but not sufficient causal factor for non-cardia gastric adenocarcinoma. A high intake of salt is strongly associated with gastric cancer. Fresh fruits and vegetables are protective but the use of vitamins and other dietary supplements does not prevent gastric cancer. Host-bacterial interaction in H. pylori infection results in different patterns of gastritis and differences in gastric acid secretion which determine disease outcome. A positive family history of gastric cancer is an important risk factor. Low serum pepsinogens reflect gastric atrophy and may be useful as a marker to identify populations at high risk for gastric cancer. H. pylori screening and treatment is a recommended gastric cancer risk reduction strategy in high-risk populations. H. pylori screening and treatment is most effective before atrophic gastritis has developed. It does not exclude the existing practice of gastric cancer surveillance in high-risk populations. In populations at low risk for gastric cancer, H. pylori screening is not recommended. First-line treatment of H. pylori infection should be in accordance with national treatment guidelines. CONCLUSION: A strategy of H. pylori screening and eradication in high-risk populations will probably reduce gastric cancer incidence, and based on current evidence is recommended by consensus.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".