Current concepts and an alternative perspective on periodontal disease
Bibliographic record
Abstract
BACKGROUND: Epidemiological data from countries worldwide show a consistent pattern implying that a fraction of around 10% of those over 40-50 years in all populations will exhibit severe periodontitis with the potential risk of losing teeth during their life-time. The subgingival microbiota shows striking similarities between populations irrespective of disease severity and can only marginally explain the clinical pattern. It is also difficult to explain this pattern by genetic and acquired risk factors such as systemic disease (e.g. diabetes) or habits (e.g. smoking) even if they may have a confounding effect on the disease. MAIN TEXT: Inflammation of the gingiva appears to be a normal and physiological response to the presence of commensal bacteria along the gingival crevice and in the dental biofilm. Over many years of exposure to the dental biofilm, the chronic inflammation in the gingiva gradually results in a loss of attachment and bone loss. Numerous laboratory and clinical studies have provided insight into the potential role of determinants that are associated with periodontitis. However, it has been difficult to relate the findings to the pattern of the distribution of the disease observed in epidemiological studies. We propose a simple and parsimonious model that considers all the multitude of potential determinants as creating effectively random noise within the dental biofilm to which the tissues react by accumulating the effects of this noise. CONCLUSIONS: We suggest that such a model can explain many of the epidemiological features of periodontal breakdown over time, and we discuss its clinical implications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".