Dental Age Estimation in Croatian Children Aged 5–14 Years
Bibliographic record
Abstract
Dental age estimation plays an important role in orthodontics and forensic dentistry. The method employed to assess dental age in this study was developed by Demirjian and his colleagues in 1973 based upon French-Canadian samples. This method is one of the most widely used methods in the world today. Objectives: The aim of this study is to evaluate the applicability of Demirjian's method from 1976 in the dental age assessment Croatian children aged 5-14. Methods: Digitalised panoramic radiographs of 1117 children of Croatian origin, 580 girls and 537 boys whose age ranged from 5 to 14 years old, were assessed using Demirjian's method. The dental ages were compared to the chronological ages through a paired t-test. Results: The results showed that Croatian children demonstrated a more advanced dental age compared to French-Canadian children as previously presented by Demirjian. The overall mean difference between the dental age and chronological age is 1.48 years in girls and 1.84 years in boys. Conclusion: The French-Canadian standards for dental age assessment provided by Demirjian are not suitable for Croatian children. Specifically, a necessity has arisen: locally based standards of dental age assessment should be established for the population of Croatia.
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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 | 0.001 |
| 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".