Poor oral health was associated with higher risk of gastric cancer: Evidence from 1431677 participants
Bibliographic record
Abstract
BACKGROUND In recent years, the association between oral health and the risk of gastric cancer (GC) has gradually attracted increased interest. However, in terms of GC incidence, the association between oral health and GC incidence remains controversial. Periodontitis is reported to increase the risk of GC. However, some studies have shown that periodontitis has no effect on the risk of GC. Therefore, the present study aimed to assess whether there is a relationship between oral health and the risk of GC. AIM To assess whether there was a relationship between oral health and the risk of GC. METHODS Five databases were searched to find eligible studies from inception to April 10, 2023. Newcastle-Ottawa Scale score was used to assess the quality of included studies. The quality of cohort studies and case-control studies were evaluated separately in this study. Incidence of GC were described by odds ratio (OR) and 95% confidence interval (CI). Funnel plot was used to represent the publication bias of included studies. We performed the data analysis by StataSE 16. RESULTS A total of 1431677 patients from twelve included studies were enrolled for data analysis in this study. According to our analysis, we found that the poor oral health was associated with higher risk of GC (OR = 1.15, 95%CI: 1.02-1.29; I 2 = 59.47%, P = 0.00 < 0.01). Moreover, after subgroup analysis, the outcomes showed that whether tooth loss (OR = 1.12, 95%CI: 0.94-1.29; I 2 = 6.01%, P > 0.01), gingivitis (OR = 1.19, 95%CI: 0.71-1.67; I 2 = 0.00%, P > 0.01), dentures (OR = 1.27, 95%CI: 0.63-1.19; I 2 = 68.79%, P > 0.01), or tooth brushing (OR = 1.25, 95%CI: 0.78-1.71; I 2 = 88.87%, P > 0.01) had no influence on the risk of GC. However, patients with periodontitis (OR = 1.13, 95%CI: 1.04-1.23; I 2 = 0.00%, P < 0.01) had a higher risk of GC. CONCLUSION Patients with poor oral health, especially periodontitis, had a higher risk of GC. Patients should be concerned about their oral health. Improving oral health might reduce the risk of GC.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".