Dental age estimation in southern Chinese population using panoramic radiographs: validation of three population specific reference datasets
Bibliographic record
Abstract
BACKGROUND: The accuracy of estimated age should depend on the reference data sets (RDS) from which the maturity scores or Ages of Attainment (AoA) were obtained. This study aimed to test the accuracy of age estimation from three different population specific dental reference datasets (RDS). METHODS: Two hundred and sixty six dental panoramic radiographs of subjects belonging to southern Chinese ethnicity were scored and dental age (DA) was estimated from three reference datasets: French-Canadian, United Kingdom (UK) Caucasian and southern Chinese. Statistical significance was set at p < 0.05 and for each method, the difference between the chronological age (CA) and dental age (CA-DA) was calculated using paired t-tests. In addition, Chi-square tests were performed to evaluate the accuracy of the age estimates within specific time interval from CA. RESULTS: The estimated age difference (CA-DA) using the French Canadian RDS was - 0.62 years for males and - 0.36 years for females. For the UK Caucasian RDS, the age difference was 0.25 years for males and 0.23 years for females. The difference observed using the southern Chinese RDS was - 0.02 years for both genders and the difference was not statistically significant (p > 0.05). The southern Chinese RDS estimated the age of 80% of subjects within ±12 months range, and 90% of subjects within ±18 months range (p < 0.05) showing it to be more accurate than other datasets. CONCLUSION: It is concluded that population specific Reference Data Sets improve the accuracy of dental age estimation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".