Demirjian Metodu Farklı Ülkelerde Yaş Tayininde Kullanılabilir mi?
Bibliographic record
Abstract
Objective: The aim of this study is to discuss the reliability and utility of Demirjian method used at calculating dental age determination with the help of using the data of other countries.Materials and Methods: The data of 22 studies, in which dental age determination was done by Demirjian method between the age of 7 - 11 age range from different countries or different regions from the same country, was examined. The inclusion standard in the studies was defined as this study; examining on healthy children, using panoramic films, researching on left bottom mandibular seven teeth. Data belong to girls and boys at the age range of 7-11 and data related with their chronological age and dental ages defined by Demirjian method was organized in tabular and the differences between dental age and chronological age were evaluated. After variance analysis at the factorial order in the data analysis, Bonferroni test was used for multiple comparisons. Type 1 error was pegged at 0,05 (α=0,05).Results: As a result of assessments, from the point of dental age designated by Demirjian method and chronological age, a difference has been detected between the children who live at different countries and live at different regions of the same country. Negative and positive differences has been detected with regards to the utility of Demirjian method by the samples taken from Netherlands, Turkey, Sudan, China, Serbia, Italy, Pakistan, New Zealand, Bosnia-Herzegovina, Malaysia and India. The difference between the results of the Belgian children who live in Romania, Iran, India and children who live in other societies were not that meaningful.Conclusion: Juvenescence and development at children differ from person to person and society to society because of reasons such as heritage, gender, hormones, nutrition, socio-cultural and environmental factors and previous diseases. Development of teeth are under the influence of familiar factors and because of this reason when scoring system in order to designate dental age of French-Canadian origin children, applied to different societies, important differences could be seen between chronological age and dental age. In order to overcome this difference, this method needs modification according to the social features.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".