Periodontitis as a risk factor for colorectal cancer: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Primary studies on the association between periodontitis and colorectal cancer (CRC) may have insufficient statistical power to reach a reliable conclusion. In this regard, the present systematic review and meta-analysis were conducted to determine whether periodontitis is a risk factor for CRC. MATERIAL AND METHODS: A systematic search was carried out in five databases, which included cohort and case-control studies published up to July 3, 2024, in which periodontitis was evaluated as a risk factor for CRC using relative risk (RR), hazard ratio (HR) or odds ratio (OR). The Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias, and the GRADE system was used to determine the certainty of the evidence. RESULTS: Of 1476 articles retrieved, 8 cohort studies were included for qualitative analysis and meta-analysis. The overall synthesis showed that periodontitis is not a risk factor for CRC (RR=1.34; 95% CI: 0.96-1.89; p<0.09; I2=95%). In addition, subgroup analyses were performed according to gender, periodontitis diagnostic methods, and risk of bias, which led to the finding of an increased risk of CRC of 32% only for men with periodontitis (RR=1.32; 95% CI: 1.16-1.50; p<0.00001; I2=0%). CONCLUSIONS: Periodontitis is not a risk factor for CRC, with very low certainty of evidence. However, the analysis of subgroups by gender showed that it is a risk factor for CRC in men, with moderate certainty of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".