Dental age assessment of 4–16year old Western Saudi children and adolescents using Demirjian’s method for forensic dentistry
Bibliographic record
Abstract
Aim Developing teeth are used to assess maturity and estimate age in several disciplines such as pediodontic, orthodontic , pediatric endocrinology and forensic odontology . The aim was to determine the statistical adjustment needed when dental age is estimated using Demirjian’s method for Western Saudi children and adolescents between ages 4 and 16 years of age. Also, to compare a Western Saudi population sample with the original French-Canadian. Methods and materials The most common standard for forensic age estimation or analysis of children and sub-adults of Demirjian et al. (1973) was used, with a total of 198 individuals (boys = 88 and girls = 110). The panoramic radiographs were used to score the seven left mandibular teeth. Results The mean difference was 1.44 to 0.64 in girls and from 0.66 to 0.77 in boys. Among girls there was a statistically significant difference for 7, 11, and 15 years ( P < 0.05). There was a statistically significant difference ( P < 0.05), in boys for age groups 8 and 13 only. On average for all ages, Western Saudi Arabia girls were 0.059 (sd = 1.26) years and the boys 0.66 (sd = 1.14) years ahead of the French-Canadian children. Conclusion New tables were developed in order to convert dental maturity calculation according to Demirjian’s method into estimated age of contemporary Western Saudi population (significant overestimation). For future research, increase in the sample size for all age ranges to establish new maturity scores and logistic curves for the studied population group and comparison with other Saudi children in rural communities found in other regions in Saudi Arabia would be ideal.
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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 | 0.001 |
| 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".