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Record W2894611426 · doi:10.1002/ajmg.a.40383

Dental and craniofacial characteristics caused by the p.Ser40Leu mutation in <i>IFITM5</i>

2018· article· en· W2894611426 on OpenAlexaff
Didem Dagdeviren, Faleh Tamimi, Brendan Lee, Reid Sutton, Frank Rauch, Jean‐Marc Retrouvey

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsShriners Hospitals for Children - CanadaMontreal Children's HospitalMcGill University
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of Health
KeywordsCraniofacialSkullDentinogenesis imperfectaOsteosclerosisMedicineAnatomyOsteolysisOsteogenesis imperfectaDentistryPathology

Abstract

fetched live from OpenAlex

Severe forms of osteogenesis imperfecta (OI) are usually caused by mutations in genes that code for collagen Type I and frequently are associated with craniofacial abnormalities. However, the dental and craniofacial characteristics of OI caused by the p.Ser40Leu mutation in the IFITM5 gene have not been reported. We investigated a 15-year-old girl with severe OI caused by this mutation. She had marked deformations of extremity long bones. There were no clinical or radiological signs of dentinogenesis imperfecta, but one tooth was missing and several teeth were impacted. Cone beam computed tomography revealed a generalized osteopenic appearance of the craniofacial skeleton, bilateral enlargement of mandibular bodies, and areas of cortical erosions. The cranial base and skull showed a generalized granular bone pattern with a mixture of osteosclerosis and osteolysis. Sphenoid and frontal sinuses were congenitally missing. Cephalometric analysis indicated a Class III growth pattern. In this case, the IFITM5 p.Ser40Leu mutation did not affect tooth structure but was associated with deformities in craniofacial bones that resemble those in the other parts of the skeleton.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.326

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 studies0.0000.001
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 head0.010
GPT teacher head0.310
Teacher spread0.300 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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