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Record W2363119134

Dental age assessment using Demirjian′s method on urumqi juveniles

2014· article· en· W2363119134 on OpenAlexaboutno aff
Yang Shuan

Bibliographic record

VenueChinese Journal of Aesthetic Medicine · 2014
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsnot available
Fundersnot available
KeywordsLogistic regressionMedicineDentistrySignificant differenceAge groupsDemographyPopulationOrthodonticsEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Objective To validate the applicability of Demirjian's method for age estimation in children and teenagers of urumqi Uighur population. Methods Digital panoramic radiographs of 362 children of urumqi origin were assessed in Demirjian's method. There were 195 boys and 167 girls involved in this study with chronological age from 3 to 16 years.The dental maturity scores(DMS) and dental ages of all the subjects were calculated.Paired t-test was performed between the chronological age and the age determined by Demirjian's method.The method of nonlinear regression was used to established the logistic model between the dental maturity score and the chronological age initially. Results The Demirjian method utilizing French-Canadian standards presented significant difference between dental age and chronological age for the sample.The results of paired t-test between chronological age and predicted age showed that there was significant difference(P0.05)in boys and girls.Demirjian's dataset is not suitable for estimating the age of 3-16 years old urumqi Uighur children.The regression equations were estabilished as follows:Y(male)=-61.119+20.965X-0.686X2,Y(female) =-74.475 +25.214X-0.901X2. Conclusion The standards of dental age described by Demirjian etal may not be suitable for urumqi children. Uygur children may need their own specific standard for an accurate estimation of chronological 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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.572
Teacher spread0.480 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueChinese Journal of Aesthetic MedicineSame topicDental Education, Practice, ResearchFrench-language works237,207