Systematic review: gastric cancer incidence in pernicious anaemia
Bibliographic record
Abstract
BACKGROUND: Pernicious anaemia (PA) has an increased risk for gastric cancer (GC). It is not established whether PA patients need to undergo endoscopic/histological follow-up. AIM: To provide a systematic overview of the literature on PA and the development of gastric cancer, to estimate the gastric cancer incidence-rate. METHODS: According to PRISMA, we identified studies on PA patients reporting the incidence of gastric cancer. Quality of studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale. Meta-analysis on annual gastric cancer incidence rates was performed. RESULTS: Twenty-seven studies met eligibility criteria. 7 studies were of high, 6 of medium, 10 of low and 4 of very low quality. Gastric cancer incidence-rates ranged from 0% to 0.2% per person-years in 7 American, from 0% to 0.5% in 2 Asiatic, from 0% to 1.2% in 11 Northern European studies and from 0% to 0.9% in 7 studies from other European countries. The incidence-rates of gastric cancer ranged from 0% to 1.2% per person-years in studies which used gastroscopy, from 0.1% to 0.9% in those based on International Classification of Disease. Heterogeneity between studies was not statistically significant at the 5% level (Chi-squared test = 17.9, P = 0.08). The calculated pooled gastric cancer incidence-rate was 0.27% per person-years. Meta-analysis showed overall gastric cancer relative risk in PA as 6.8 (95% CI: 2.6-18.1). CONCLUSIONS: This systematic review shows a pooled gastric cancer incidence-rate in pernicious anaemia of 0.27% per person-years and an estimated nearly sevenfold relative risk of gastric cancer in pernicious anaemia patients. Further high quality studies are needed to confirm this higher risk.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".