Validation of demirjian's 8-teeth method of age estimation in the population of Bengaluru
Bibliographic record
Abstract
Introduction: Demirjian's method of dental age assessment estimates the overall dental age by scoring based on the stage of tooth formation, using panoramic radiographs. This method was primarily based on data acquired from individuals of French-Canadian origin. It has since been applied and modified for the Indian population. Therefore, the aim of the present study is to assess the reliability of the Indian formula in the population of Bengaluru. Materials and Methods: 's 8-teeth method and the chronological age of each subject was calculated. Pearson's correlation, Independent student test/Mann-Whitney test and Chi-square test were used for statistical analysis of the results obtained. Results and Discussion: < 0.001) between the chronological and estimated age by Demirjian's method was obtained. The mean absolute error among the total samples was not significant and majority of the error in the estimated age was <1 year in males and females, indicating that the India specific formula gave nearly accurate estimation of the chronological age of the sample subjects. Conclusion: Demirjian's Indian formula is relaible in the population of Bengaluru.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".