MétaCan
Menu
Back to cohort

The oro‐dental phenotype in Prader–Willi syndrome: a survey of 15 patients

2007· article· en· W2093861094 on OpenAlexaff
Isabelle Bailleul‐Forestier, Veroniek Verhaeghe, Jean‐Pierre Fryns, Frans Vinckier, Dominique Declerck, Annick Vogels

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineShort staturePediatricsBody mass indexOral hygieneOverweightObesityDentistryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Prader-Willi syndrome (PWS) is a rare disorder caused by genetic defects in certain regions of chromosome 15q11-13. It is characterized by severe neonatal hypotonia and feeding problems, childhood-onset hyperphagia and obesity, short stature, facial dysmorphy, hypogonadism, learning and behavioural difficulties, and dental abnormalities. AIM: To describe the oro-dental phenotypic spectrum of patients with PWS. DESIGN: Fifteen PWS patients (3-35 years of age) being followed at the Centre for Human Genetics of the University Hospital of Leuven were examined at the dental clinic of the same institution. Medical information collected included age at diagnosis, body mass index (BMI) and level of cognitive functioning. Oral, clinical and radiological evaluations were performed. Caries experience (cavitation level), dental erosion and salivary flow rates were assessed. RESULTS: The 15 patients had dmft/DMFT scores ranging from 0 to 28, while nine were cavity-free. Those with severe caries experience also presented advanced dental erosion. BMI ranged from 16 to 42.6. There was no association between BMI and caries experience or erosive tooth wear. The PWS patients in our survey presented with a more favourable oral health status than those in previous studies. This might be due to early diet management or better oral hygiene during childhood or both.

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)0.001
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 score0.008
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.270
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.

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

Citations40
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Paediatric DentistrySame topicGenetic Syndromes and ImprintingFrench-language works237,207