Radiological Evaluation of the Accuracy of Demirjian, Nolla, and Willems Methods for Dental Age Estimation in 3–17‐Year‐Old Iranian Children
Bibliographic record
Abstract
Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient’s developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3–17 in the northern Iranian population. Methods: This cross‐sectional study examined panoramic radiographs of 434 children aged 3–17 from Mazandaran Province, Iran, who had teeth 31–37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t ‐test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years ( P < 0.001) in girls and 0.09 years ( P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years ( P = 0.47) in girls and 0.12 years ( P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years ( P < 0.001) in girls and 0.40 years ( P < 0.001) in boys. The accuracy of each method varied with the patient’s age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3–17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".