Comparison of two methods of dental age estimation in 7–15‐year‐old Malays
Bibliographic record
Abstract
BACKGROUND: Numerous methods of age estimation have been proposed. The Demirjian method is the most frequently used, which was first applied in a French Canadian population in 1973. The Willems method is a modification of the above and was applied in a Belgian population in 2002. OBJECTIVES: The objectives of this study were to test the applicability of the two methods, namely Demirjian and Willems, for age estimation in a Malay population, and to find the correlation between body mass index and the difference between the dental age and the chronological age. MATERIALS AND METHODS: A cross-sectional study involving 214 boys and 214 girls, selected by a simple stratified random sampling method was carried out. The orthopantomograph was used to score the seven left mandibular teeth, and the calculated maturity score was used to obtain the Demirjian dental age. Willems dental age was estimated using the tables proposed in the Willems method. Results. The Demirjian method overestimated the age by 0.75 and 0.61 years, while the Willems method overestimated the age by 0.55 and 0.41 years among boys and girls, respectively. In boys, the body mass index was significantly correlated to the difference in age using the Willems method. CONCLUSION: Further modification of either method is indicated for dental age estimation among the Malay population.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".