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The genotype–phenotype correlation of hereditary multiple exostoses

2006· article· en· W2155445061 on OpenAlexaff
Christine M. Alvarez, Stephen J. Tredwell, Mary A. De Vera, Michael R. Hayden

Bibliographic record

VenueClinical Genetics · 2006
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsPacific Centre for Reproductive MedicineBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsHereditary multiple exostosesPhenotypeGenotypeMutationGeneticsGenotype-phenotype distinctionMedicineDiseaseCorrelationGeneBiologyInternal medicine

Abstract

fetched live from OpenAlex

Hereditary multiple exostoses (HME) is an autosomal dominant condition with a wide spectrum of clinical presentations. The purpose of this study was to determine the relationship between the genotype and the phenotype in HME. Thirty-two affected individuals from 10 families participated in the study. An extensive description of HME phenotype in terms of the anatomical burden of disease involved clinical and radiographic examinations and evaluation of 76 parameters. Mutations were determined by sequencing the EXT 1 and EXT 2 genes. Mutations were found in eight families (26 individuals), with one mutation previously reported in the literature and seven novel mutations. There were seven subjects with an EXT 1 mutation and 16 with an EXT 2 mutation. Patients with EXT 1 mutation were found to have more exostoses, more limb malalignment with shorter limb segments and height, and more pelvic and flatbone involvement. A genotype-phenotype correlation exists in HME, with patients with EXT 1 mutations having a higher degree of anatomical burden.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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".

Quick stats

Citations79
Published2006
Admission routes1
Has abstractyes

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