Dental age estimation using Willems method: A cross-sectional study on children in a North Indian city
Bibliographic record
Abstract
Background: Numerous methods of age estimation have been proposed. The Demirjian method which was first applied in a French-Canadian population in 1972 is the most frequently used dental age estimation method. However, a constant over estimation using this method has been reported by numerous researchers. The Willems method is a modification of the above and was first applied in a Belgian Caucasian population in 2002. Several studies in the recent years found Willems method to be more accurate than the original Demirjians method. Objectives: The objective of this study was to evaluate the applicability of Willems method for dental age estimation in 6 -15-year-old North Indian children. Methods: The sample for this cross sectional study consisted of 390 OPGs of (215 boys and 175 girls) randomly selected from the patient records of a post graduate training dental college in North India. Exact chronological age of the subjects (in years and months) was calculated. The OPGs were used to score the seven mandibular teeth and dental age was estimated using the tables proposed in Willems method. Results: Significant correlation between the chronological age and dental age estimated using Willems method was observed in both males ( r = 0.90) and females ( r = 0.87). The mean difference between estimated DA and CA for males was 0.004 ± 1.08 years ( P = 0.94). While for females, it was 0.031 ± 1.18 years ( P = 0.72). The results showed no statistically significant difference between chronological age and dental age estimated using Willems method in the study population. Conclusion: Willems dental age estimation method without any modification can predict the chronological age of 6-15 year old North Indian children with good accuracy.
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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 | 0.002 |
| 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".