Age estimation in three distinct east Asian population groups using southern Han Chinese dental reference dataset
Bibliographic record
Abstract
BACKGROUND: Dental age estimation can assist in the identification of victims following natural disasters and it can also help to solve birth date disputes in individuals involved in criminal activities. A reference dataset (RDS) has been developed from the dental development of 2306 subjects of southern Han Chinese origin and subsequently validated. This study aimed to test the applicability of the southern Han Chinese dental maturation RDS on three distinct East Asian population groups. METHODS: A total of 953 dental panoramic radiographs of subjects aged 2 to 24 years were obtained from Philippines, Thailand and Japan. The staging of dental development was conducted according to Anglo-Canadian classification system. The dental age (DA) was calculated using six methods; one un-weighted average and five weighted average (n-tds, sd-tds, se-tds, 1/sd-tds, 1/se-tds) methods based on the scores of the southern Han Chinese RDS. Statistical significance was set at p < 0.05 and the variation between chronological age (CA) and DA was evaluated using paired t-test and Bland & Altman scatter plots. RESULTS: From six dental age calculations, all methods of DA accurately estimated the age of Japanese and few methods in Filipino subjects (n-tds, 1/sd-tds, 1/se-tds). There was consistent overestimation of age for all the methods for Thai females (p < 0.05). CONCLUSIONS: The southern Han Chinese dental reference dataset was shown to be most accurate for Japanese, followed by Thai males and it was particularly ineffective for Filipinos and Thai females.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".