Dental and skeletal maturation as simultaneous and separate predictors of chronological age in post-pubertal individuals: a preliminary study in assessing the probability of having attained 16 years of age in the living
Bibliographic record
Abstract
The goal of this paper is to explore whether combining dental and skeletal maturation data increases the reliability of determining whether an individual is 16 years old or older. This is tentatively done by building probabilistic models for age estimation based on dental and skeletal maturation using a longitudinal sample of eight males, with annual assessments between the ages of 13 and 19, totalling 56 observations. Skeletal maturity was assessed for the radius and ulna using the TW2 method, and dental maturity was assessed for the second and third molars using Demirjian’s scheme. Logistic regression was selected to determine the probability of an individual being 16 years of age and older, by combining dental and skeletal maturity scores and using them separately. The age estimation models combining dental and skeletal maturity scores seem to perform better than either dental or skeletal maturity in isolation. In addition, when in isolation or combination, models based on skeletal maturity scores seem to outperform models based on dental maturity scores. The findings seem to support the notion that dental development is less reliable than skeletal maturity for age estimation in adolescents, but these results have to be confirmed by further studies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".