Accuracy of population‐specific Demirjian curves in the estimation of dental age of Saudi children
Bibliographic record
Abstract
BACKGROUND: The Demirjian eight-stage method is one of the principal methods used to quantify the degree of maturity from age 3 to 17. Aim. The objective of this study was to compare the accuracy of dental age of different population-specific curves, derived using the Demirjian method, to the chronological age of Saudi children aged between 4 and 14. DESIGN: Panoramic radiographic records of 176 children (91 boys and 85 girls), without any history of systemic disease, were assessed using the Demirjian method, and the dental age was calculated using curves designed for French-Canadian, Belgian, Kuwaiti, and Saudi children. The difference from chronological age (DA-CA) for each curve was then statistically compared using ANOVA, and each of the curves was compared to the chronological age using multinomial regression modelling. RESULTS: The results suggest that although population-specific curves are more accurate in the prediction of age, a considerable variation within each population still exists. CONCLUSIONS: The Demirjian method offers great scope in fields that require the study of the pattern of growth rather than the accuracy of age estimation.
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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 | 0.003 | 0.011 |
| 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 | 0.000 |
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".