A proposed community caries index of treatment need derived from the international caries detection and assessment system
Bibliographic record
Abstract
OBJECTIVE: To examine the accuracy of a short version of the International Caries Detection and Assessment System (ICDAS) in predicting caries treatment need for children. METHODS: The study is a validation study using data from three previously published cross-sectional studies. Participants were children with different dentitions from Kuwait, Brazil, and Spain. Children were clinically examined using ICDAS criteria. Children were classified into preventive, non-operative, and operative categories. Sensitivity and specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic (ROC) curve were used to measure the discriminative and diagnostic accuracy of the proposed short version of ICDAS compared to the full ICDAS. RESULTS: Clinical dental examination data from a total of 3076 children aged 1-15 years were used. The proposed short ICDAS and the full ICDAS showed a very good agreement on caries treatment need determination with Kappa scores of more than 0.833 in all dentitions. The short ICDAS showed excellent operating characteristics in all dentitions. The area under the ROC was more than 90% in primary dentition, 89% in permanent dentition, and 86% in mixed dentition in different populations. Lowest area under ROC and sensitivity values were observed when discriminating between non-operative and operative treatment categories. CONCLUSIONS: The proposed short version of the ICDAS showed good diagnostic accuracy in classifying children according to their caries treatment need. By reducing the number of surfaces examined and the time needed for clinical assessment, the short version of the ICDAS is a convenient alternative to the full ICDAS to be used in community settings.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".