What is the quantitative risk of gastric cancer in the first-degree relatives of patients? A meta-analysis
Bibliographic record
Abstract
AIMTo quantify the risk of gastric cancer in first-degree relatives of patients with the cancer. METHODSA comprehensive literature search was performed.Case-control trials comparing the frequency of a positive family history of gastric cancer in patients with gastric cancer, vs 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. RESULTSThirty-two relevant studies out of 612 potential abstracts (n = 80690 individuals) were included.19.0% of the patients and 10.9% of the controls had at least one relative with gastric cancer (P < 0.00001).The pooled relative risk for the development of gastric cancer in association with a positive family history was 2.35 (95%CI: 1.96-2.81).The Cochran Q test for heterogeneity was positive (P < 0.00001, I ² = 92%).After excluding the three outlier studies with the highest relative risks, heterogeneity remained significant (P < 0.00001, I ² = 90%).The result was not different among Asian studies as compared to others and remained
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".