Dental age assessment among Tunisian children using the Demirjian method
Bibliographic record
Abstract
CONTEXT: Since Demirjian system of estimating dental maturity was first described, many researchers from different countries have tested its accuracy among diverse populations. Some of these studies have pointed out a need to determine population-specific standards. AIM: The aim of this study is to evaluate the suitability of the Demirjian's method for dental age assessment in Tunisian children. MATERIALS AND METHODS: This is a prospective study previously approved by the Research Ethics Local Committee of the University Hospital Fattouma Bourguiba of Monastir (Tunisia). Panoramic radiographs of 280 healthy Tunisian children of age 2.8-16.5 years were examined with Demirjian method and scored by three trained observers. STATISTICAL ANALYSIS USED: Dental age was compared to chronological age by using the analysis of variance (ANOVA) test. Cohen's Kappa test was performed to calculate the intra- and inter-examiner agreements. RESULTS: Underestimation was seen in children aged between 9 and 16 years and the range of accuracy varied from -0.02 to 3 years. The advancement in dental age as determined by Demirjian system when compared to chronological age ranged from 0.3 to 1.32 year for young males and from 0.26 to 1.37 year for young females (age ranged from 3 to 8 years). CONCLUSIONS: The standards provided by Demirjian for French-Canadian children may not be suitable for Tunisian children. Each population of children may need their own specific standard for an accurate estimation of chronological age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".