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Record W2915035778 · doi:10.1002/jbm4.10174

Osteogenesis Imperfecta: New Perspectives From Clinical and Translational Research

2019· review· en· W2915035778 on OpenAlexaff
Josephine T. Tauer, Marie‐Eve Robinson, Frank Rauch

Bibliographic record

VenueJBMR Plus · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsShriners Hospitals for Children - Canada
Fundersnot available
KeywordsOsteogenesis imperfectaSclerostinBisphosphonateBone morphogenetic proteinMedicineBone mineralTransforming growth factor betaBioinformaticsGeneInternal medicineCancer researchReceptorBiologyGeneticsOsteoporosisPathologyWnt signaling pathway

Abstract

fetched live from OpenAlex

ABSTRACT Osteogenesis imperfecta (OI) is a monogenic bone fragility disorder that usually is caused by mutations in one of the two genes coding for collagen type I alpha chains, COL1A1 or COL1A2. Mutations in at least 18 other genes can also lead to an OI phenotype. As genetic testing is more widely used, mutations in these genes are also more frequently discovered in individuals who have a propensity for fractures, but who do not have other typical clinical characteristics of OI. Intravenous bisphosphonate therapy is still the most widely used drug treatment approach. Preclinical studies in OI mouse models have shown encouraging effects when the antiresorptive effect of a bisphosphonate was combined with bone anabolic therapy using a sclerostin antibody. Other novel experimental treatment approaches include inhibition of transforming growth factor beta signaling with a neutralizing antibody and the inhibition of myostatin and activin A by a soluble activin receptor 2B. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.268
GPT teacher head0.525
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations154
Published2019
Admission routes1
Has abstractyes

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