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Record W2128750848 · doi:10.1093/ejo/cji010

Dental age in Dutch children

2005· article· en· W2128750848 on OpenAlexaboutno aff
I. H. Leurs, E. Wattel, Irene H. A. Aartman, EJ Etty, Birte Prahl‐Andersen

Bibliographic record

VenueEuropean Journal of Orthodontics · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConfidence intervalDemographyMedicineLogistic regressionKappaAge groupsPopulationCohen's kappaDentistryMathematicsStatisticsGeometryInternal medicine

Abstract

fetched live from OpenAlex

Dental age was studied in a sample of 451 Dutch children (226 boys and 225 girls) according to the method of Demirjian. They were born between 1972 and 1993 and were between 3 and 17 years of age at the time a dental pantomogram (DPT) was obtained. All children were placed in the age group closest to their chronological age. All 451 DPTs were scored by one examiner. A subset of 52 DPTs was scored by a second examiner and the intra-class correlation coefficient (ICC) and Cohen's kappa were calculated. The ICC was 0.99 and Cohen's kappa 0.68. Boys and girls were analysed separately.A significant difference was found between chronological age and dental age. On average, the Dutch boys were 0.4 years and the girls 0.6 years ahead of the French-Canadian children analysed by Demirjian. Therefore, the French-Canadian standards were not considered suitable for Dutch children. New graphs for the Dutch population were constructed using a logistic curve with the equation Y = 100*{1/(1 + e(-alpha(x - x0)))} as a basis. The 90 per cent confidence interval was calculated. To determine whether the logistic curve was correct, a residual analysis was carried out and scatter plots of the differences were made. The explained variance was 93.9 per cent for the boys and 94.8 per cent for the girls. Both the residual analysis and the scatter plots indicated that the logistic curve was appropriate for use with Dutch children. In addition to the graphs, tables were produced which transfer the maturity scores calculated by the method of Demirjian into Dutch 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.121
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations162
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of OrthodonticsSame topicForensic Anthropology and Bioarchaeology StudiesFrench-language works237,207