Periodontitis and systemic diseases: a record of discussions of working group 4 of the Joint EFP/AAP Workshop on Periodontitis and Systemic Diseases
Bibliographic record
Abstract
BACKGROUND: There has been an explosion in research into possible associations between periodontitis and various systemic diseases and conditions. AIM: To review the evidence for associations between periodontitis and various systemic diseases and conditions, including chronic obstructive pulmonary disease (COPD), pneumonia, chronic kidney disease, rheumatoid arthritis, cognitive impairment, obesity, metabolic syndrome and cancer, and to document headline discussions of the state of each field. Periodontal associations with diabetes, cardiovascular disease and adverse pregnancy outcomes were not discussed by working group 4. RESULTS: Working group 4 recognized that the studies performed to date were largely cross-sectional or case-control with few prospective cohort studies and no randomized clinical trials. The best current evidence suggests that periodontitis is characterized by both infection and pro-inflammatory events, which variously manifest within the systemic diseases and disorders discussed. Diseases with at least minimal evidence of an association with periodontitis include COPD, pneumonia, chronic kidney disease, rheumatoid arthritis, cognitive impairment, obesity, metabolic syndrome and cancer. The working group agreed that there is insufficient evidence to date to infer causal relationships with the exception that organisms originating in the oral microbiome can cause lung infections. CONCLUSIONS: The group was unanimous in their opinion that the reported associations do not imply causality, and establishment of causality will require new studies that fulfil the Bradford Hill or equivalent criteria. Precise and community-agreed case definitions of periodontal disease states must be implemented systematically to enable consistent and clearer interpretations of studies of the relationship to systemic diseases. The members of the working group were unanimous in their opinion that to develop data that best inform clinicians, investigators and the public, studies should focus on robust disease outcomes and avoid surrogate endpoints. It was concluded that because of the relative immaturity of the body of evidence for each of the purported relationships, the field is wide open and the gaps in knowledge are large.
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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 | 0.192 | 0.173 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.048 | 0.071 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".