Demirjian Stage Tooth Formation Results from a Large Group of Children
Bibliographic record
Abstract
The aim of this study is to present further data on the timing and variation of individual permanent mandibular teeth using Demirjian stages from a large collaboration. Seven mandibular permanent teeth were assessed from dental radiographs of healthy dental patients from Australia, Belgium, Canada, England, Finland, France, South Korea and Sweden (cross-sectional study; n = 9,371, 4,710 males, 4,661 females; aged 2–18). Data are presented in three ways, namely by tooth stage for males, females, and pooled sex. Mean age at entry of each tooth formation stage (maturity data) was calculated using logistic regression and modified for age prediction. The 51% confidence interval for age within stage of individual tooth stages was calculated for use in forensic age estimation where the burden of proof is on the balance of probabilities. Average age, standard deviation, standard error, 3rd and 97th percentile within tooth stage was calculated from a uniform age sample (171 for each year of age from 3 to 16, n = 2,394). Modified maturity data and average age within stage from the uniform age distribution are two new methods of age estimation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".