Identifying a Potential Summary Measure for Overall Caries Level in Children Examined with the International Caries Detection and Assessment System
Bibliographic record
Abstract
OBJECTIVE: To identify a potential summative summary measure that reflects a patient's overall caries level in children examined with the International Caries Detection and Assessment System (ICDAS). SUBJECTS AND METHODS: Participants were 1- to 15-year-old children from Kuwait, Brazil, and Spain. Children's teeth were examined using the ICDAS. Multiple measures of central tendency and dentition-specific indices were considered as potential summative measures. The relationship between the summative measures and number of caries lesions was evaluated considering degrees of caries severity using Spearman's correlation analysis. The results were generated using the Kuwaiti sample and were cross-validated using the Brazilian and Spanish samples. RESULTS: A total of 2,808 children participated in the present study. Total ICDAS score and mean ICDAS score showed a strong correlation with the number of caries lesions at different caries severity levels in primary, mixed, and permanent dentitions. The total ICDAS score of 51 buccal (B), 61B, 54 occlusal (O), 55O, 64O, 65O, 74O, 75O, 84O, and 85O surfaces in primary dentition and the total ICDAS score of 14O, 16 lingual (L), 16O, 24O, 26L, 26O, 36B, 36O, 37O, 46O, 46B, and 47O surfaces in permanent dentition or mixed dentition (if present) showed strong correlations with the number of caries lesions at different caries severity levels. CONCLUSIONS: Total ICDAS and mean ICDAS scores were the best summary measures of overall caries level at different dental stages. Total ICDAS scores of selectively examined 10 surfaces in primary dentition and 12 surfaces in permanent dentition can give an excellent summary measure for a patient's overall caries level with high diagnostic accuracy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".