Periodontal disease as a risk factor for sporadic colorectal cancer: results from COLDENT study
Bibliographic record
Abstract
Colorectal cancer remains the top leading cancer worldwide. Accumulating evidence suggests periodontal pathogens are involved in colorectal carcinogenesis, indicating the need for high-quality epidemiological evidence linking periodontal disease (PD) and colorectal cancer (CRC). Thus, we conducted the first population-based case-control study that was specifically designed to investigate the association between compromised oral health and sporadic CRC. A total of 348 incident cases of colon or rectal cancer, and 310 age and sex frequency-matched controls, from the Montreal island and Laval population participated in the study. Data were collected on PD and on several CRC risk factors using validated questionnaires. A life-course approach was used to document long-term history regarding lifestyle factors. Multivariable unconditional logistic regression analysis was used to estimate the rate ratio (RR) quantifying the association between CRC and PD. Results showed that the rate of new diagnosis of CRC in persons with a positive history of PD was 1.45 times higher than in those with a negative history of PD adjusting for age, sex, BMI, education, income, diabetes, family history of CRC, regular use of non-steroidal anti-inflammatory drugs, lifetime cumulative smoking, lifetime consumption of red meats, processed meats, and alcoholic drinks, and lifetime total physical activity score (adjusted RR = 1.45; 95% CI 1.04-2.01; p = 0.026). Our results support the hypothesis of an association between PD and sporadic 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".