Dental age estimation of Omani children using Demirjian's method
Bibliographic record
Abstract
Dental age plays a significant role in forensic dentistry, orthodontics and paediatric dentistry, as well as in general diagnosis and treatment planning. Different methods have been developed to determine dental age. One of the most commonly used methods is Demirjian’s method, which was developed in 1973 from research on a large number of French-Canadian children. It is based on the degree of tooth mineralisation by examining the radiological appearance of the lower mandibular left quadrant. The purpose of this study was to assess the dental age of Omani children using Demirjian's method and evaluate the applicability of the method in dental age estimation for Omani children. The sample consisted of 485 digital panoramic radiographs of children (264 males, 221 females) aged between 4.6 years and 16.5 years, and obtained from the records of the Military Dental Centre in Oman. The data were analysed using SPSS. Paired t-tests, intraclass correlation coefficients (ICC) and difference-against-mean plots were used to compare the dental age calculated by Demirjian's method with chronological age. A single examiner scored the radiographs, and intra-observer reliability was evaluated using Cronbach's alpha on data from rescoring one out of every 20 radiographs. For boys, the mean difference between chronological age and dental age for all age groups was 0.10 (95% CI −0.03 to 0.24). For girls, the mean difference between chronological age and dental age for all age groups was 0.05 (95% CI −0.11 to 0.22). Difference-against-mean plots showed no evidence of differential bias by age. For boys, the ICC was 0.896 (95% CI 0.869–0.917); for girls, it was 0.886 (95% CI 0.854–0.911). Difference-against-mean plots for boys (Fig. 1) and girls (Fig. 2) showed some evidence of differential bias by age. In conclusion, the extent of the observed differences was sufficient for doubt to be cast upon the utility of Demirjian’s method for Oman, particularly when it is considered that the method’s most likely application would be in age determination for minors in the workforce.
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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 | 0.001 | 0.012 |
| 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 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".