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Record W1986580482 · doi:10.1172/jci33612

Building bone to reverse osteoporosis and repair fractures

2008· review· en· W1986580482 on OpenAlexaff
Sundeep Khosla, Jennifer J. Westendorf, Merry Jo Oursler

Bibliographic record

VenueJournal of Clinical Investigation · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingNational Institutes of Health
KeywordsOsteoporosisMedicineBone healingAnabolic AgentsBone morphogenetic proteinBone morphogenetic protein 7TeriparatideBone morphogenetic protein 2AnabolismBone Density Conservation AgentsBone remodelingBioinformaticsSurgeryBone mineralInternal medicineBiology

Abstract

fetched live from OpenAlex

An important, unfilled clinical need is the development of new approaches to improve fracture healing and to treat osteoporosis by increasing bone mass. Recombinant forms of bone morphogenetic protein 2 (BMP2) and BMP7 are FDA approved to promote spinal fusion and fracture healing, respectively, and the first FDA-approved anabolic drug for osteoporosis, parathyroid hormone, increases bone mass when administered intermittently but can only be given to patients in the US for two years. As we discuss here, the tremendous explosion over the last two decades in our fundamental understanding of the mechanisms of bone remodeling has led to the prospect of mechanism-based anabolic therapies for bone disorders.

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)0.000
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.072
GPT teacher head0.417
Teacher spread0.345 · 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

Citations349
Published2008
Admission routes1
Has abstractyes

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