Dental Versus Skeletal Methods for Age Estimation in Growing Individuals: A Systematic Review
Bibliographic record
Abstract
Accurate age estimation is crucial in forensic, clinical, and legal contexts. Dental age (DA) and skeletal age (SA) are commonly used markers, yet their relative accuracy across populations remains unclear. This systematic review aimed to compare the accuracy of DA and SA estimation methods relative to chronological age (CA) in children and adolescents. A comprehensive search of PubMed, Scopus, EBSCOhost, and the Cochrane Library identified studies evaluating dental methods such as Demirjian, Willems, Cameriere, and Nolla, as well as skeletal methods including Fishman, Greulich and Pyle, cervical vertebral maturation (CVM), and Gilsanz-Ratib. Studies involving individuals aged 6-19 years were analyzed, and the risk of bias was assessed using the modified Newcastle-Ottawa Scale. Nine studies from diverse populations, including Turkey, Egypt, and South Africa, were included. The findings revealed that DA methods, particularly Willems and Nolla, demonstrated a higher correlation with CA than SA methods, which often underestimated age in older children. Accuracy varied according to age, sex, and population, while combined approaches improved reliability. Overall, DA estimation methods generally outperform SA techniques in early to mid-adolescents, though population-specific calibration and combined DA+SA approaches enhance accuracy. Careful method selection remains essential in forensic and clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".