Validity of Willems age estimation method in children’s & adolescents’ of Dakshina Kannada Region, India
Bibliographic record
Abstract
Willems gender-specific and non-specific maturity scores derive the age of the individual with acceptable error margins in the Belgian Population. This needs to be validated in various subpopulations to enable its use in age estimation. Our aim was to test the validity of both Willems’s I & II maturity scores to estimate dental age in children and adolescents (aged 5–20 yrs) in Dakshina Kannada population in India. Seven teeth of mandibular left quadrant from 800 Orthopantomograms (Male:Female::372:428) of individuals aged 5–20 years were staged using Demirjian’s method and substituted by the Willems maturity scores to derive the age. The chronological and the predicted age of the individuals were correlated to derive the mean absolute error of age estimation in male, female and total. The mean absolute error of estimation of age in males, females, and total of Willems I and II techniques were 1.11, 1.04, 1.07, 1.15, 1.04 and 1.09 years respectively. The gender nonspecific scores predominantly underestimated the age. The Willems I technique showed an underestimation of age in females and over-estimation of age in males (p < 0.001). The present study validates Willems method of age estimation in Dakshina Kannada population, showing a low error rate of ∼1 year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".