A Test of the Demirjian method of dental ageing using a mixed population sample from Northern Ontario
Bibliographic record
Abstract
Dentition is commonly used to determine age because disease and nutritional factors minimally impact dental maturation. This study evaluated the use of Demirjian’s method of estimating age for a population from Sudbury, Ontario, Canada. A sample of 245 panoramic radiographs, taken from male and female children aged 5–16 years of varying ancestries, was collected from two local dental practices. Eight radiographs were excluded due to distortion or congenitally missing teeth. The results indicate a general over-estimation of age across all ages, with the only significant under-estimation of age evident in 15 and 16 year-olds. Inter- and intra-observer agreement has been documented for both boys and girls. This study yielded a Sudbury-specific set of standards for applying Demirjian’s method of dental ageing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| 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".