Is periodontal disease associated with increased risk of colorectal cancer? A meta‐analysis
Bibliographic record
Abstract
Abstract Objective Colorectal cancer (CRC) is a commonly diagnosed cancer. Previous studies investigating the relationship between periodontal disease (PD) and CRC showed controversial conclusions. This meta‐analysis is to explore the association between PD and CRC. Methods Observational studies that explore the association between CRC and PD were included in this meta‐analysis. A comprehensive literature search in 7 electronic databases to identify all relevant studies published prior to February 2020. The Newcastle‐Ottawa Quality Assessment Scale was used to evaluate the quality each study. The pooled relative risk (RR) and 95% confidence interval (CI) were used to estimate the association between the PD and CRC risk. Results A total of 838 articles were obtained from database searching and 4 articles were obtained from other sources, and 13 articles with 14 studies involving 634,744 participants were included. An association between PD and increased CRC incidence was found, periodontal disease patients were 21% (95%CI, [1.06, 1.38], I2=83.9%) more likely to develop colorectal cancer than people with healthy oral cavity. But there is no significant association between PD and CRC mortality. The heterogeneity of this meta‐analysis was relatively high, I2 was 83.9% (95%CI, [72.7%, 90.5%]), chi‐squared of Q test was 62.18, but sensitivity analysis confirmed the stability of the result. Funnel plot, Eggerʼs test and Beggʼs test found no publication bias of analysis. Conclusion The current meta‐analysis demonstrates an association between PD and CRC, indicating that early CRC screening is necessary for people with poor oral health, and oral health improvement might be beneficial for reducing CRC risk.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.061 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".