Leisure-time physical activity and gastric cancer risk: a pooled study within the Stomach cancer Pooling (StoP) Project
Bibliographic record
Abstract
Abstract Background Physical activity (PA) has been recognized as a protective factor against several types of cancer, though robust evidence related to Gastric Cancer (GC) are lacking. This study aimed to establish whether leisure-time PA can prevent GC using data from a large pooled analysis of case-control studies within the Stomach cancer Pooling (StoP) Project.Methods Five case-control studies from StoP project collected data on PA, for a total of 2,415 cases and 9,722 controls. Subjects were classified into three leisure-time PA categories, either none/low, intermediate or high, based on study-specific tertiles. We used a two-stage approach. Firstly, we applied multivariable logistic regression models to obtain study-specific odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Afterwards, we used a random-effect models for estimating pooled effect estimates. Heterogeneity across studies was assessed using Q and I 2 statistics. We performed stratified analyses according to demographic, lifestyle and clinical covariates.Results The pooled ORs for GC risk were 0.90 (95% CI: 0.72, 1.13) for intermediate, and 0.72 (95% CI: 0.57, 0.91) for high levels of leisure-time PA. There was no evidence of significant heterogeneity in outcome estimates ( I 2 = 49.7%; p=0.094 for intermediate; I 2 = 42.9%, p=0.135 for high levels of exposure). GC risk estimates did not differ across strata of selected covariates.Conclusions Our study is the largest pooled analysis that provides insights about protective effects of high levels of recreational PA on GC risk. Although our results should be confirmed from large cohort studies, the implications have relevant public health significance.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".