Individual age estimation using pulp-to-tooth area ratio in single-rooted teeth
Bibliographic record
Abstract
Dental age estimation can play a crucial role in forensic investigations as it can assist authorities in the identification of living and deceased individuals. Of the various age estimation methods based on odontology, pulp-to-tooth ratios measured through radiography have been a popular choice due to their less invasive nature. Pulp-to-tooth area ratios were assessed in 12 permanent single rooted teeth (maxillary and mandibular, left and right central incisors, lateral incisors, and canines) using clinical cone beam computed tomographs. A total of 227 teeth were analyzed from 66 subjects, 33 males and 33 females. Pulp-to-tooth area ratios were measured in the coronal, sagittal and axial perspectives. Additionally, the difference in pulp-to-tooth area ratio between the entire pulp cavity area of the tooth, and the pulp cavity area exclusively above the cementoenamel junction were compared. Bilateral symmetry was confirmed between the teeth from the right and left side (p = 0.9405). All correlations exceeded 0.6, with the highest correlations observed in maxillary teeth (R=-0.836), the teeth from females (R=-0.830), and central and lateral incisors exclusively (R=-0.802). The standard error of estimates from the linear regression models varied between ±10.11 and 14.98 years. This study confirmed that pulp-to-tooth ratios are a sound technique to estimate age.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".