Caries and Periodontitis: Contesting the Conventional Wisdom on Their Aetiology
Why this work is in the frame
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Bibliographic record
Abstract
We review the literature on the oral microbiome and the role of the microbiota in the development of dental caries and periodontitis. While most research has been focused on identifying one or more specific determinants of these diseases, the results have provided limited predictive value and have not been able to explain the variation in the distribution of these diseases observed in epidemiological or clinical studies. Drawing on existing knowledge about the nature of the oral microbiota, we suggest that a stochastic model based on the Weiner process provides simple and parsimonious explanations for the pathogenesis of both caries and periodontitis, making few assumptions, and providing explanations for phenomena that have hitherto proved difficult, or have required complex arguments, to explain. These diseases occur as the result of the dental hard tissues and periodontal tissues integrating the random "noise" caused by normal metabolic activities of commensal microorganisms in the dental biofilm. The processes that result in the progression and regression of caries and periodontitis may be considered as "natural," rather than pathological, even if, when left unchecked over long periods of time, they can result in the development of pathologies. The likelihood of progression or regression can be influenced by other determinants, but these processes will nevertheless occur in the absence of such influences. The distributional characteristics of the model approximate the findings of epidemiological studies indicating that, for both caries and periodontitis, there will be few sites affected in the early period after the eruption of the permanent dentition, but in those older there is an almost linear relationship with increasing age; furthermore, the longer a site survives without being affected, the less likely that it will be affected. We discuss the clinical and public health importance of these findings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it