Age estimation in older adults: Use of pulp/tooth ratios calculated from tooth sections
Bibliographic record
Abstract
OBJECTIVES: Accurate age estimates are foundational for bioarchaeological research, yet the ability to accurately age older adult skeletons remains elusive. This study uses a new version of pulp/tooth area calculations to investigate chronological age of older archaeological individuals. MATERIALS AND METHODS: Pulp/tooth area ratios were calculated on modern control teeth (n = 10) that were first radiographed and then sectioned for comparative analysis. Pulp/tooth area ratios were determined on sectioned teeth using ImageJ software for: (a) modern individuals of known age (n = 26); (b) individuals from Belleville, Ontario, Canada (1821-1874) with documented age (n = 50); and (c) Belleville individuals with skeletally estimated age (n = 122). RESULTS: Calculations from tooth sections on modern teeth (n = 10) resulted in a mean absolute error (MAE) of ±3.9 years, whereas the radiographic method for the same teeth had an MAE of ±14.45 years. Results indicate that sectioned pulp/tooth area ratios are a significant predictor of chronological age (p < .005), with MAEs of ±4.53 years for Belleville and ±3.77 years for modern individuals. There were no statistically significant differences in age estimations between modern and archaeological individuals, or with respect to tooth type, sex, or intra/inter-observer estimations. DISCUSSION: This study provides a new more accurate method for estimating age-at-death, particularly for individuals in the 50+ age category. Sectioning the teeth and directly measuring exposed pulp chambers results in age estimations that were within ±4.15 years for both modern and archaeological individuals, thus presenting a method that will enhance the ability to age older individuals.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".