What is the quantitative risk of gastric cancer in the first-degree relatives of patients? A meta-analysis
Bibliographic record
Abstract
AIM: To quantify the risk of gastric cancer in first-degree relatives of patients with the cancer. METHODS: non-gastric cancer controls were retrieved. Studies with missed or non-extractable data, studies in children, abstracts, and duplicate publications were excluded. A meta-analysis of pooled odd ratios was performed using Review Manager 5.0.25. We performed subgroup analysis on Asian studies and a sensitivity analysis based on the quality of the studies, type of the outcome, sample size, and whether studies considered only first-degree relatives. RESULTS: < 0.00001). CONCLUSION: Individuals with a first-degree relative affected with gastric cancer have a risk of about 2.5-fold for the development of gastric cancer. This could be due to genetic or environmental factors. Screening and preventive strategies should be developed for this high-risk population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".