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Development of a Clinical Dental Reference Data Set for Dubai Emirati Children and Adolescents Based on the Demirjian Method

2025· article· en· W4415987643 on OpenAlexaboutno aff
Sharifa AlHaj, Manal Al Halabi, Mawlood Kowash, Anas Salami, Ammar H. Khamis, Iyad Hussein

Bibliographic record

VenueInternational Journal of Clinical Pediatric Dentistry · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsReference dataSet (abstract data type)Reference valuesAlternative medicineDental research

Abstract

fetched live from OpenAlex

Original researchthese reasons, a sequence of dental age estimation methods has been developed primarily based on the mineralization stage of tooth germs. 9any authors have developed scoring methods to assess dental age using dental calcification stages of permanent teeth.Examples of these methods include Demirjian et al., 10 Nolla, 11 Willems et al., 12 and Moorrees et al. 13 methods.The most widely used dental maturity staging system is the method developed by Demirjian in 1973 on a sample of French-Canadian children. 14,15Demirjian method 10 is based on eight stages of tooth mineralization, from calcification of the cusp to closure of the apex, on the seven left IntroductIonAge is a period of human life that is measured by the chronological passing of days, weeks, months, and years from birth.Biological age refers to how old a person seems; accordingly, biological age assessment can be achieved by various techniques, including physical examination, cervical vertebral maturation, 1 handwrist radiographs, dental examination, and also by using dental panoramic tomography (DPT) images. 2 In forensic medicine, age estimation is no longer only beneficial in the identification of deceased victims or human remains but additionally in age estimation of living individuals, which would possibly be required in such cases where chronological age plays a necessary role, for example, criminal liability, immigration, school attendance, and social benefits. 3In dental and medical practice, age estimation is regarded to be of outstanding importance. 4,5or example, pediatric dentists and orthodontists must be capable of understanding a child's growth and developmental status represented by both the chronological and dental age, as it is important to lead to a more accurate diagnosis and treatment planning. 6Dental age assessment is commonly based on observing the stage of tooth eruption and the degree of mineralization of the developing tooth buds' enamel from radiographs. 7The process of maturation is much more uniform, progressive, sequenced, and continuous compared to tooth eruption, and is much less affected by endocrine disease, dietary deficiency conditions, and environmental changes. 8These factors influence tooth development and may show differences in chronological age compared to dental age.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.150
GPT teacher head0.492
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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