The risk of gastric cancer in patients with gastric intestinal metaplasia in 5‐year follow‐up
Bibliographic record
Abstract
BACKGROUND: Gastric intestinal metaplasia (GIM) is the premalignant stage of gastric cancer; however, consensus on its management has not been established. AIM: To determine the risk factors for gastric cancer in a population of patients with GIM to guide the appropriate clinical recommendations in a low prevalence area for gastric cancer. METHODS: This was a retrospective cohort study. Ninety-one patients with GIM diagnosed between 2004 and 2014 were recruited for surveillance EGD every 6-12 months until a diagnosis of gastric cancer or completion of the planned 5-year follow-up duration. Possible risk factors for gastric cancer were assessed. RESULTS: At initial presentation, 81 of the 91 patients (89%) had complete GIM, whereas the remaining 11% had a study entry diagnosis of incomplete GIM. No cancer developed amongst patients with complete GIM. In contrast, five of the 10 patients exhibiting incomplete GIM (50%) progressed to high-grade dysplasia (n=2) or cancer (n=3). Male gender (P=.027), and incomplete GIM (P=.001) were associated with high-risk histology (dysplasia or cancer) by study end. A trend suggested a possible association with smoking (P=.08). CONCLUSION: Male patients and those with incomplete GIM are at greatest risk of developing dysplasia or early gastric cancer. Further studies in determining optimal surveillance intervals and impact on cancer incidence and mortality are still required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".