Accuracy of Demirjian and Willems Methods for Age Estimation of Children from Northern Argentina
Bibliographic record
Abstract
The dental development is widely used to estimate the chronological age; one method frequently used is that of Demirjian, applied in Franc-Canadian children, and the other is that of Willems, adapted from the first one to Belgian children. Demirjian uses scores according to the degree of calcification of the seven permanent mandible teeth of the left side; Willems adapts to years the scale of scores of Demirjian. Objective: To analyze the accuracy in determining the chronological age through the degree of dental calcification using the methods of Demirjian and of Willems in children from Tucumán, Argentina. Methods: 66 children (29 female and 37 male) who assisted to radiological studies previous to the dental treatment were selected. Panoramic X-rays were taken. Dental ages were calculated using the corresponding tables of the methods of Demirjian and Willems. Chronological ages were calculated between the date of birth and the date of the study. The statistical paired t-test was used. Results: Through the method of Demirjian the mean of the differences was 0.44 ± 0.96 for girls and 0.49 ± 1.02 for boys, being significant differences between both genders. The method of Willems was more accurate than that one of Demirjian (-0.08 ± 0.92 for girls and 0.19 ± 0.94 for boys), being no significant differences between the dental and the chronological ages. Conclusion: According to these results both methods could be used to estimate the chronological age through the observed dental calcification in radiographic images of children from northern Argentina. Nevertheless, greater statistical accuracy with the method of Willems would be reached for both genders.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".