Assessment of dental maturity of children aged 3.5 to 13.5 years using the Demirjian method in an Iranian population
Bibliographic record
Abstract
Radiographs of developing teeth are commonly used to assess dental maturity. The method for assessment of dental maturity first described by Demirjian is widely used and accepted. The aim of this study was to assess the accuracy of the Demirjian method in an Iranian population in order to compare the difference in dental maturity between these children with the data obtained in Canadian children and to determine whether there is a correlation between dental maturity and BMI-for-age. In this study, the orthopantomographs of 519 healthy children (264 boys and 255 girls) aged 3.5-13.5 years were reviewed and the dental age was determined by the Demirjian method. The chronological ages of the children were obtained by subtracting their birthdates from the date of taking the radiograph. Height and weight measurements were also recorded and the data were analyzed using SPSS-16 software. The Demirjian method overestimated the age by 0.15 and 0.21 years in boys and girls, respectively. Paired t-test analysis showed that these differences were statistically significant (P = 0.001). The increase in mean age difference initiated from the underweight group towards the overweight group, but this correlation was not statistically significant (P = 0.094). Based on the amount of differences between estimated dental age and chronological age in this investigation, the Demirjian method seems to be clinically applicable in the Iranian 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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".