A Longitudinal Study of Premalignant Gastric Lesions and Early Onset Gastric Cancer Among Young Adults in Central Saudi Arabia
Bibliographic record
Abstract
Gastric cancer traditionally affects older adults, and its precursor lesions and risk factors are well-documented in this population. Helicobacter pylori (H. pylori) infection remains highly prevalent in Saudi Arabia and contributes to gastric pathology. However, early-onset gastric cancer (EOGC), diagnosed in individuals aged ≤ 45 years, presents unique challenges and remains poorly understood in young populations. Therefore, we conducted an observational cohort study using a prospective longitudinal design (2021–2024) involving 1823 Saudi nationals aged 18–45 years who underwent zoom high-definition chromoendoscopy to evaluate the prevalence of premalignant gastric lesions (PGLs) and EOGC. We found a high H. pylori prevalence (78.0%) with PGLs in 1.9% of participants and EOGC-adenocarcinoma in 0.7% of patients. All EOGC cases arose from dysplasia, with most PGLs being classified as OLGA/OLGIM stage II/III. Multiple risk factorswere significantly associated with PGLs and EOGC, including H. pylori infection (p = 0.022), increasing age (p < 0.001), a family history of gastric cancer (p < 0.001), poor dietary habits (p < 0.001), obesity (p < 0.001), and smoking (p < 0.001). Additional EOGC risk factors include dage of 36–45 years (p = 0.018), EBV infection (p = 0.016), and diabetes mellitus (p = 0.001). These findings demonstrate the notable presence of PGLs and EOGC in young Saudi adults and emphasize the importance of early detection and risk factor management in this vulnerable population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".