Age estimation using dental radiography in the Brazilian population: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Age estimation using dental radiography plays a critical role in forensic and legal contexts. This study aimed to perform a systematic review and meta-analysis to investigate the evidence-based support for various methods of age estimation using dental radiography in Brazilians and to evaluate the precision of these methods. Main body The search strategy was performed in 5 electronic databases and in gray literature for articles published until August 3th, 2024. Two independent reviewers performed data extraction and methodological quality using an adapted version of Newcastle − Ottawa Scale. To estimate the precision of different dental methods, the mean absolute error between the age estimated by dental methods and the chronological age was calculated. The mean difference between the two variables was used as the effect measure. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. A total of 28 studies met the eligibility criteria. One study was rated 4 (low quality), 16 studies received scores between 5 and 6 (moderate quality), and 11 studies scored 7 or above (high quality). Fourteen studies were included in the meta-analysis, allowing the evaluation of different dental methods for age estimation in male and female individuals. All methods showed a mean error (expressed in mean difference in chronological age) of less than 2 years. For males, the mean error ranged from 0.2 to 1.75 years. For females, the mean error ranged from 0.01 to 1.27 years. Conclusions The methods tended to overestimate the actual chronological age. Significant limitations were found regarding the representativeness of the Brazilian population in the evaluated studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.160 | 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".