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Record W4247068466 · doi:10.1111/bjd.20279

PA03: Understanding phenotypic variations of pseudohypoparathyroidism and loss‐of‐function <i>GNAS</i> variants

2021· article· en· W4247068466 on OpenAlexaff
N.-Y Kang, Jennifer Harrington, Pekka Kannus, Elena Pope, Irene Lara‐Corrales

Bibliographic record

VenueBritish Journal of Dermatology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsGNAS complex locusPseudohypoparathyroidismPhenotypeMedicineGeneticsInternal medicineBiologyParathyroid hormone

Abstract

fetched live from OpenAlex

N.-Y.C. Kang,1 J. Harrington,1,2 P. Kannu,1,3 E. Pope1,3 and I. Lara-Corrales1,4 1Faculty of Medicine, University of Toronto, Toronto, ON, Canada; and 2Department of Endocrinology, 3Division of Clinical and Metabolic Genetics, The Hospital for Sick Children and 4Section of Dermatology, The Hospital for Sick Children, Toronto, ON, Canada Pseudohypoparathyroidism (PHP) refers to a heterogeneous group of endocrine disorders that occur as a result of loss-of-function (LOF) mutations in GNAS. There is a characteristic parental inheritance pattern, phenotype and hormone profile associated with each PHP subtype [PHP-1a, PHP-1b, pseudopseudohypoparathyroidism (PPHP) and progressive osseous heteroplasia (POH)]. However, not all patients with PHP and LOF GNAS mutations demonstrate the characteristic phenotype and hormone profile. The aim of this study was to describe the phenotypic variability of patients with PHP and LOF GNAS mutations and to explore the genotype–phenotype correlation. This was a single-centre retrospective review, which included patients with a positive genetic test for a LOF GNAS mutation. We collected patients’ baseline characteristics (sex, ethnicity, family and medical histories), diagnostic history (including age at diagnosis and clinical findings), available genetic, laboratory and radiology results, including genetic tests, biochemistry, X-ray findings and treatments. Twenty-five patients with LOF GNAS mutations were included in our study. PHP-1a, PPHP, PHP-1b and POH were diagnosed in 52%, 28%, 16% and 4% of patients, respectively. The most common mutations in PHP-1a, PHP-1b, PPHP and POH were splicing (38%), methylation defect (100%), missense (43%) and frameshift (100%) mutations, respectively. Of the 13 patients with PHP-1a, common findings were parathyroid hormone (PTH) and thyroid-stimulating hormone resistance (100%), short stature (85%), brachydactyly type E (77%), round faces (69%), obesity (61%) and speech/language delays (61%). Of the four patients with PHP-1b, 67% had PTH resistance, short stature and brachydactyly type E. The seven patients with PPHP did not have endocrine abnormalities, but 80% had short stature, atrophic skin lesions and osteoma cutis, and 75% had speech and language delays. The patient with POH had atrophic skin lesions but no hormone resistance. PHP subtypes are characterized by specific phenotypes: PHP-1a with Albright hereditary osteodystrophy (AHO), developmental delay and hormone resistance; PHP-1b with PTH resistance only; PPHP with AHO only; and POH with none of the above. However, we found that patients with PHP-1a did not demonstrate all AHO features, patients with PHP-1b had AHO and patients with PPHP had language delays and AHO. Furthermore, the same mutation – but not phenotype – was found among the different PHP subtypes. Thus, we concluded that there are phenotypic variations and a lack of a genotype–phenotype correlation in PHP.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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