Dental Maturity Curves in Finnish Children: Demirjian's Method Revisited and Polynomial Functions for Age Estimation
Bibliographic record
Abstract
Dental maturity was studied from 2213 dental panoramic radiographs of healthy ethnic Finns from southern Finland, aged between 2 and 19 years. The aim was to provide new Finnish maturity tables and curves and to compare the efficiency of Demirjian's method when differently weighted scores and polynomial regressions are used. The inter-ethnic variations lead us to calculate specific Finnish weighted scores. Demirjian's method gives maturity score as a function of age and seems better adapted for clinicians because, in their case, the maturity score is unknown. Polynomial functions give age as a function of maturity score and are statically adapted for age estimation studies. Finnish dental maturity tables and development curves are given for Demirjian's method and for polynomial functions. Sexual dimorphism is established for the same weighted score for girls and boys, and girls present a greater maturity than boys for all of age groups. Polynomial functions are highly reliable (0.19% of misclassifies) and the percentile method, using Finnish weighted scores, is very accurate (+/- 1.95 years on average, between 2 and 18 years of age). This suggests that polynomial functions are most useful in forensic sciences, while Demirjian's method is most useful for dental health clinicians.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".