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Record W2088323843 · doi:10.3109/03008207.2013.867957

Multiple hereditary exostoses (MHE): elucidating the pathogenesis of a rare skeletal disorder through interdisciplinary research

2014· article· en· W2088323843 on OpenAlexfundno aff
Kevin B. Jones, Maurizio Pacifici, Matthew J. Hilton

Bibliographic record

VenueConnective Tissue Research · 2014
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesUniversity of British Columbia
KeywordsHereditary multiple exostosesMalignancyMedicinePathogenesisCartilage oligomeric matrix proteinExtracellular matrixBioinformaticsTranslational researchPathologyBiologyOsteoarthritisGeneticsSurgeryOsteochondromaAlternative medicine

Abstract

fetched live from OpenAlex

Abstract An interdisciplinary and international group of clinicians and scientists gathered in Philadelphia, PA, to attend the fourth International Research Conference on Multiple Hereditary Exostoses (MHE), a rare and severe skeletal disorder. MHE is largely caused by autosomal dominant mutations in EXT1 or EXT2, genes encoding Golgi-associated glycosyltransferases responsible for heparan sulfate (HS) synthesis. HS chains are key constituents of cell surface- and extracellular matrix-associated proteoglycans, which are known regulators of skeletal development. MHE affected individuals are HS-deficient, can display skeletal growth retardation and deformities, and consistently develop benign, cartilage-capped bony outgrowths (termed exostoses or osteochondromas) near the growth plates of many skeletal elements. Nearly 2% of patients will have their exostoses progress to malignancy, becoming peripheral chondrosarcomas. Current treatments are limited to the surgical removal of symptomatic exostoses. No definitive treatments have been established to inhibit further formation and growth of exostoses, prevent transition to malignancy, or address other medical problems experienced by MHE patients, including chronic pain. Thus, the goals of the Conference were to assess our current understanding of MHE pathogenesis, identify key gaps in information, envision future therapeutic strategies and discuss ways to test and implement them. This report provides an assessment of the exciting and promising findings in MHE and related fields presented at the Conference and a discussion of the future MHE research directions. The Conference underlined the critical usefulness of gathering experts in several research fields to forge new alliances and identify cross-fertilization areas to benefit both basic and translational biomedical research on 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 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)0.003
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.106
GPT teacher head0.440
Teacher spread0.334 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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