Pre-existing diabetes mellitus increases the risk of gastric cancer: A meta-analysis
Bibliographic record
Abstract
AIM: To systematically assess the association between diabetes and incidence of gastric cancer. METHODS: We searched MedLine (PubMed), EMBASE, and the Cochrane Library without any limitations with respect to publication date or language, we also searched the references of qualifying articles. Case-control studies and cohort studies comparing the risk of gastric cancer between diabetic patients and control subjects were included. We excluded studies reporting only standardized incidence ratios without control groups and those that investigated only mortality but not incidence. Seventeen studies met our criteria, and the qualities of these studies were assessed using the Newcastle-Ottawa Quality Assessment Scale. We performed a meta-analysis of pre-existing diabetes and gastric cancer incidence using the DerSimonian-Laird method for random-effects. For subgroup analyses, we separated the studies by study type, region, sex and method to determine confounding factors and reliability. We also conducted subgroup analyses to examine the effects of smoking, Helicobacter pylori (H. pylori) infection, and cancer site. Publication bias was evaluated using Begg's test. RESULTS: A random-effects model meta-analysis showed an increased gastric cancer risk in diabetic patients [relative risk (RR) = 1.19; 95%CI: 1.08-1.31]. Subgroup analyses indicated that this result persisted in cohort studies (RR = 1.20; 95%CI: 1.08-1.34), in studies on populations of both Western (RR = 1.18; 95%CI: 1.03-1.36) and Eastern countries (RR = 1.19; 95%CI: 1.02-1.38), in a female subgroup (RR=1.24; 95%CI: 1.01-1.52), and in highly qualified studies (RR = 1.17; 95%CI: 1.05-1.31). Moreover, these results persisted when the analysis was confined to studies adjusted for well-known gastric cancer risk factors such as smoking (RR = 1.17; 95%CI: 1.01-1.34) and H. pylori infection (RR = 2.35; 95%CI: 1.24-4.46). CONCLUSION: Pre-existing diabetes mellitus may increase the risk of gastric cancer by approximately 19%. This effect seems to be unrelated to geographical region.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.059 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".