The Natural History of Dental Caries Lesions
Bibliographic record
Abstract
Dental caries is a ubiquitous disease affecting all age groups and segments of the population. It is known that not all caries lesions progress to cavitation, but little is known regarding the progression pattern of caries lesions. This study's purpose was to evaluate the natural history of dental caries using a standardized, visually based system, the International Caries Detection and Assessment System (ICDAS). The study population consisted of 565 consenting children, who were enrolled and examined at baseline and at regular intervals over 48 months with ICDAS and yearly bitewing radiographs. Of these, 338 children completed all examinations. Not all lesions cavitated at the same rate, differing by surface type and baseline ICDAS severity score and activity status. With increasing severity, the percentage of lesions progressing to cavitation increased: 19%, 32%, 68%, and 66% for ICDAS scores 1, 2, 3, and 4, respectively. Lesions on occlusal surfaces were more likely to cavitate, followed by buccal pits, lingual grooves, proximal surfaces, and buccal and lingual surfaces. Cavitation was more likely on molars, followed by pre-molars and anterior teeth. Predictors of cavitation included age, gender, surfaces and tooth types, and ICDAS severity/activity at baseline. In conclusion, characterization of lesion severity with ICDAS can be a strong predictor of lesion progression to cavitation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".