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Record W1985924068 · doi:10.1016/s0377-5291(12)70012-3

The Accuracy of Demirjian Method in Dental Age Estimation of Malay Children

2011· article· en· W1985924068 on OpenAlexaboutno aff
Saifeddin Abu Asab, Siti Noor Fazliah Mohd Noor, Mohd Fadhli Khamis

Bibliographic record

VenueSingapore Dental Journal · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMalayMedicineDentistryAge groupsOrthodonticsDemography

Abstract

fetched live from OpenAlex

This study is aimed to evaluate the accuracy of Demirjian method in estimating the chronological age of male and female Kelantanese Malay children between 6 and 16 years of age and to establish a new dental age (DA) curve if the Demirjian method was not found to be accurate. About 905 panoramic radiographs of healthy Malay children between 6 and 16 years of age were collected from the radiographic unit in the Hospital Universiti Sains Malaysia (HUSM) and the orthodontic clinic in Hospital Kota Bharu (HKB). Children who had any disease affecting the dental development, or have agenesis in the lower arch and poor quality radiographic images were excluded. The results showed that Demirjian method overestimated the chronological age (CA) by 1.23 years for boys and 1.20 years for girls and it was less accurate for the Kelantanese Malay children. Thus new standard curve were produced and tested on external samples. Results showed that the mean difference between the chronological age and DA is about 0.17 years for boys and 0.11 years for girls. DA was more advanced in the Kelantanese Malay boys and girls as compared to French-Canadian children in all age groups. It is concluded that the Demirjian method tends to be less accurate in estimating the chronological age in Malay children. The new curve that was produced is more applicable to the Kelantanese Malay children.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.296
Teacher spread0.261 · 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.

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".

Quick stats

Citations41
Published2011
Admission routes1
Has abstractyes

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