Association between the frequency of tooth brushing and esophageal carcinoma risk: an update systematic review and meta-analysis
Bibliographic record
Abstract
Background: Lower frequency of tooth brushing was thought to be associated with esophageal carcinoma (EC). However, some researchers suggested that this association did not exist or had not yet reached statistical significance. The purpose of this study was to calculate a more precise estimation of the relationship between the frequency of tooth brushing and the risk of EC by combining the results between different studies using the meta-analysis. Methods: We searched the PubMed, Embase, Web of science, and Scopus electronic databases up to July 2021. According to PECO approach (Population, Exposure, Comparator and Outcomes), we assessed the association between tooth brushing frequency and EC risk which reported the adjusted risk ratios (adjRR), hazard ratios (adjHR), or odds ratios (adjOR) with 95% confidence interval (CI). The random effects model was used to quantitatively evaluate the combined results. Two researchers independently evaluated the risk bias of the included studies using the Newcastle-Ottawa Scale (NOS). The robustness of results was evaluated by subgroup analysis, sensitivity analysis, and publication bias. Results: In total, we identified 13 case-control studies with 14 reports which included 16,761 participants and 5,671 patients. Pooled results showed the lowest frequency of brushing was significantly associated with an increased risk of EC in comparison to the highest (adjOR: 2.00, 95% CI: 1.61–2.48). There was moderate heterogeneity among included studies (P=0.001, I2=61.4%). The original studies included in this meta-analysis were all case-control studies. Study quality was all moderate or above based on NOS score ranges of 6 stars or more. Conclusions: Available evidence suggests a low frequency of tooth brushing may be an important risk factor for EC. However, higher quality studies should continue to be conducted to investigate the optimal threshold of brushing frequency for the prevention of EC.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".