Dental maturity of Saudi children: Role of ethnicity in age determination
Bibliographic record
Abstract
PURPOSE: Demirjian's dental maturity scores and curves have been widely used for human age determination. Several authors have reported considerable differences between the true and estimated age based on the Demirjian curves, which have been accounted for by ethnicity. The purpose of the current study was to assess the role of ethnicity-specific dental maturation curves in age estimation of Saudi children. MATERIALS AND METHODS: A sample of 452 healthy Saudi children aged 4 to 14 years were aged based on the original French-Canadian Demirjian curves and several modified Demirjian curves specified for certain ethnic groups: Saudi, Kuwaiti, Polish, Dutch, Pakistani, and Belgian. One-way ANOVA and a post hoc Scheffé's test were used to assess the differences between chronological age and dental age estimated by the different curves (P<0.05). RESULTS: The curves designed for Dutch, Polish, Saudi, and Belgian (5th percentile) populations had a significantly lower error in estimating age than the original French-Canadian and Belgian (50th percentile) curves. The optimal curve for males was the Saudi one, with a mean absolute difference between estimated age and chronological age of 8.6 months. For females, the optimal curve was the Polish one, with a mean absolute difference of 7.4 months. It was revealed that accurate age determination was not related to certain ethnicity-specific curves. CONCLUSION: We conclude that ethnicity might play a role in age determination, but not a principal one.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".