Salt intake and gastric cancer: a pooled analysis within the Stomach cancer Pooling (StoP) Project
Bibliographic record
Abstract
Previous studies show that consuming foods preserved by salting increases the risk of gastric cancer, while results on the association between total salt or added salt and gastric cancer are less consistent and vary with the exposure considered. This study aimed to quantify the association between dietary salt exposure and gastric cancer, using an individual participant data meta-analysis of studies participating in the Stomach cancer Pooling (StoP) Project. Data from 25 studies (10,283 cases and 24,643 controls) from the StoP Project with information on salt taste preference (tasteless, normal, salty), use of table salt (never, sometimes, always), total sodium intake (tertiles of grams/day), and high-salt and salt-preserved foods intake (tertiles of grams/day) were used. A two-stage approach based on random-effects models was used to pool study-specific adjusted (sex, age, and gastric cancer risk factors) odds ratios (aORs), and the corresponding 95% confidence intervals (95% CI). Gastric cancer risk was higher for salty taste preference (aOR 1.59, 95% CI 1.25–2.03), always using table salt (aOR 1.33, 95% CI 1.16–1.54), and for the highest tertile of high-salt and salt-preserved foods intake (aOR 1.24, 95% CI 1.01–1.51) vs. the lowest tertile. No significant association was observed for the highest vs. the lowest tertile of total sodium intake (aOR 1.08, 95% CI 0.82–1.43). The results obtained were consistent across anatomic sites, strata of Helicobacter pylori infection, and sociodemographic, lifestyle and study characteristics. Salty taste preference, always using table salt, and a greater high-salt and salt-preserved foods intake increased the risk of gastric cancer, though the association was less robust with total sodium intake.
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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 | 0.032 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".