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Record W2810102057 · doi:10.1038/s41436-018-0045-1

Growth characteristics in individuals with osteogenesis imperfecta in North America: results from a multicenter study

2018· article· en· W2810102057 on OpenAlexafffund
Mahim Jain, Allison Tam, Jay R. Shapiro, Robert D. Steiner, Peter A. Smith, Michael B. Bober, Tracy Hart, David Cuthbertson, J. Krischer, Mary A. Mullins, Sunil Bellur, Peter H. Byers, Melanie Pepin, Michaela Durigova, Francis H. Glorieux, Frank Rauch, Brendan Lee, V. Reid Sutton, David R. Eyre, Deborah Krakow, Laura L. Tosi, Cathleen Raggio, Eric Orwoll, Eric T. Rush, Sandesh C.S. Nagamani

Bibliographic record

VenueGenetics in Medicine · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsShriners Hospitals for Children - CanadaMcGill University
FundersDavid Geffen School of Medicine, University of California, Los AngelesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of General Medical SciencesUniversity of California, Los AngelesNational Institutes of HealthChildren's National HospitalHospital for Special SurgeryNational Center for Advancing Translational SciencesBanting and Best Diabetes Centre, University of TorontoOsteogenesis Imperfecta FoundationIntellectual and Developmental Disabilities Research CenterShriners Hospitals for ChildrenUniversity of WashingtonUniversity of Nebraska Medical CenterDoris Duke Charitable Foundation
KeywordsOsteogenesis imperfectaMedicineBody mass indexGrowth curve (statistics)Short statureCohortBody heightPediatricsInternal medicineStandard scoreBody weightDemographyMathematics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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

Citations54
Published2018
Admission routes2
Has abstractno

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