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Surface Anatomy of the Face in Down's Syndrome: Age-Related Changes of Anthropometric Proportion Indices in the Craniofacial Regions

2002· article· en· W2087420369 on OpenAlexaff
Leslie G. Farkas, Marko Katić, Christopher R. Forrest

Bibliographic record

VenueJournal of Craniofacial Surgery · 2002
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsSunnybrook Health Science CentreHospital for Sick Children
Fundersnot available
KeywordsMedicineCraniofacialAnthropometryNoseOrthodonticsAnatomyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of the study was to determine the age-related changes in normal and abnormal proportion indices in Down's syndrome patients between 1 to 5, 6 to 15, and 16 to 36 years of age. Nine indices were analyzed in five craniofacial regions of 125 subjects: 70.2% to 79.0% were normal and 21.0% to 29.8% abnormal. Proportionate indices increased in frequency in all age groups in a much higher percentage than disproportionate ones. In the oldest group, the frequency of normal proportions significantly increased in the head and trended higher in the facial, orbital, and nasal regions, and lower in the ear. Disproportions showed a similar pattern, with a decreasing frequency in all regions except the ear. The variations of harmony and disharmony in normal proportions, and moderate and severe in disproportions, helped clarify the morphological changes influencing the craniofacial design. Among proportionate ratios, harmony ranged from 45% to 65.5% in age group 1 and from 30% to 87.5% in group 3. No cases of severe subnormal disproportions were seen in the nasal tip protrusion-nose width and nasal root depth indices in age group 1, and none in the cephalic index, midface-lower face depth, and nasal tip protrusion-nose width indices in group 3. The 20-year age range of group 3 helped show the post-maturation improvements of the face and the remedying effect of the extended growth rate on some initial facial disproportions.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.293
Teacher spread0.256 · 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".

Quick stats

Citations32
Published2002
Admission routes1
Has abstractyes

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