Estimation of dental age in panoramic radiographs in children and adoloescents from 7-17 years through Demirjian method, Cuenca – Ecuador.
Bibliographic record
Abstract
Age is an indicator of dental somatic maturation, of clinical importance in dentistry for planning the treatment of growing patients. The Demirjian method is the most widely disseminated to estimate dental age. With this method, numerous studies have been carried out in different ethnic groups, analyzing European, Asian and American children, among others. These results suggest possible differences in dental maturation patterns between different populations. The aim of the present study was to correlate the dental age, using the Demirjian method, with the chronological age according to gender in a group of children who attended the Radiological Center “Innova” located in Cuenca-Ecuador in the period 2012 - 2014, This study was descriptive, retrospective and analytical, in which a sample stratified by age and gender was selected, including 362 radiographs, of which 205 are female and 157 male. An inter-class correlation coefficient was obtained, as a measure of correlation between dental and chronological age, of 0.830 in the female gender and 0.801 in the male gender. In general, Demirjian’s method tended to underestimate dental age in girls and boys in a similar way. In conclusion, the results obtained in the present work do not coincide with many places in the world, that is, the dental age in this group was slightly lower in both girls and boys than the original Franco - Canadian sample. The obtained range of dental age is not similar to the chronological age but the degree of correlation between both is adequate to apply in our population, presenting itself in the same way according to gender, so a standard table for the population was elaborated Cuencana. We consider that the Demirjian method is applicable to this sample of children using the database proposed in the present investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".