Presence of the Periodontal Bacterium Porphyromonas gingivalis in Patients With and Without Cancer: A Meta-analysis
Bibliographic record
Abstract
A high presence of periodontal pathogens such as Porphyromonas gingivalis (P. gingivalis) has been found in cancers at different body locations. The imbalance of the oral microbiome (dysbiosis) promotes the chronic inflammatory process that could contribute to carcinogenesis. To assess the P. gingivalis detection in cancer patients. A search for studies on P. gingivalis and neoplasms was conducted from 1974 to 2021 in the following databases: PubMed (MEDLINE), Cochrane Library, Web of Science (WoS), and Scopus. For dichotomous outcomes, the estimates of effects of an intervention were expressed as odds ratios (OR) using Mantel-Haenszel (M-H) method with 95% confidence intervals. Funnel plot and Egger’s test for publication bias analysis were used. Also, Newcastle-Ottawa (NOS) studies methodological quality assessment scale was employed. Thirteen studies that involved 1732 cancer patients and 3298 controls without cancer were included in this meta-analysis. P. gingivalis detection was 1.81 times more likely in cancer patients (p<0.01) compared to controls. P. gingivalis detection was also more likely in patients with colorectal (OR: 2.00, p=0.02) or pancreatic (OR: 1.32, p=0.02) tumors. In contrast, oral, esophageal, lung, or breast cancers did not show a significant increase of P. gingivalis detection (p>0.05).
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.068 |
| 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".