Comparison of Dental Maturity in Children of Different Ethnic Origins: International Maturity Curves for Clinicians
Why this work is in the frame
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Bibliographic record
Abstract
Dental maturity was studied with 9577 dental panoramic tomograms of healthy subjects from 8 countries, aged between 2 and 25 years of age. Demirjian's method based on 7 teeth was used for determining dental maturity scores, establishing gender-specific tables of maturity scores and development graphs. The aim of this study was to give dental maturity standards when the ethnic origin is unknown and to compare the efficiency and applicability of this method to forensic sciences and dental clinicians. The second aim was to compare the dental maturity of these different populations. We noted an high efficiency for International Demirjian's method at 99% CI (0.85% of misclassified and a mean accuracy between 2 to 18 years +/- 2.15 years), which makes it useful for forensic purposes. Nevertheless, this international method is less accurate than Demirjian's method developed for a specific country, because of the inter-ethnic variability obtained by the addition of 8 countries in the dental database. There are inter-ethnic differences classified in three major groups. Australians have the fastest dental maturation and Koreans have the slowest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it