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Record W2990433723 · doi:10.1038/s41436-019-0688-6

Pediatric Outcomes Data Collection Instrument is a Useful Patient-Reported Outcome Measure for Physical Function in Children with Osteogenesis Imperfecta

2019· article· en· W2990433723 on OpenAlexafffund
Chaya N. Murali, David Cuthbertson, Brady Slater, Dianne Nguyen, Alicia Turner, Gerald E. Harris, V. Reid Sutton, Brendan Lee, Frank Rauch, Francis H. Glorieux, Jean‐Marc Retrouvey, Paul W. Esposito, Eric T. Rush, Michael B. Bober, David R. Eyre, Danielle Gomez, Tracy Hart, Mahim Jain, Deborah Krakow, Jeffrey P. Krischer, Eric Orwoll, Cathleen Raggio, Peter A. Smith, Laura L. Tosi, Sandesh C.S. Nagamani

Bibliographic record

VenueGenetics in Medicine · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsMcGill UniversityShriners Hospitals for Children - CanadaMontreal Children's Hospital
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesNational Center for Advancing Translational SciencesBanting and Best Diabetes Centre, University of TorontoOsteogenesis Imperfecta FoundationIntellectual and Developmental Disabilities Research CenterNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Dental and Craniofacial ResearchChildren's National HospitalUniversity of California, Los AngelesRare Diseases Clinical Research NetworkNational Institutes of HealthUniversity of Nebraska Medical Center
KeywordsMedicinePhysical therapyPromPatient-reported outcomeOsteogenesis imperfectaPopulationQuality of life (healthcare)

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.319
Teacher spread0.278 · 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 teacher head, 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

Citations27
Published2019
Admission routes2
Has abstractno

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