The Accuracy of Demirjian Method in Dental Age Estimation of Malay Children
Bibliographic record
Abstract
This study is aimed to evaluate the accuracy of Demirjian method in estimating the chronological age of male and female Kelantanese Malay children between 6 and 16 years of age and to establish a new dental age (DA) curve if the Demirjian method was not found to be accurate. About 905 panoramic radiographs of healthy Malay children between 6 and 16 years of age were collected from the radiographic unit in the Hospital Universiti Sains Malaysia (HUSM) and the orthodontic clinic in Hospital Kota Bharu (HKB). Children who had any disease affecting the dental development, or have agenesis in the lower arch and poor quality radiographic images were excluded. The results showed that Demirjian method overestimated the chronological age (CA) by 1.23 years for boys and 1.20 years for girls and it was less accurate for the Kelantanese Malay children. Thus new standard curve were produced and tested on external samples. Results showed that the mean difference between the chronological age and DA is about 0.17 years for boys and 0.11 years for girls. DA was more advanced in the Kelantanese Malay boys and girls as compared to French-Canadian children in all age groups. It is concluded that the Demirjian method tends to be less accurate in estimating the chronological age in Malay children. The new curve that was produced is more applicable to the Kelantanese Malay children.
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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 | 0.002 | 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 | 0.001 |
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".