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Record W2128750848 · doi:10.1093/ejo/cji010

Dental age in Dutch children

2005· article· en· W2128750848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
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 score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.029
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