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Record W2902807692 · doi:10.1016/s2213-8587(18)30308-5

Cortical and trabecular bone microarchitecture as an independent predictor of incident fracture risk in older women and men in the Bone Microarchitecture International Consortium (BoMIC): a prospective study

2018· article· en· W2902807692 on OpenAlexaff
Elizabeth J. Samelson, Kerry E Broe, Hanfei Xu, Laiji Yang, Steven K. Boyd, Emmanuel Biver, Paweł Szulc, Jonathan D. Adachi, Shreyasee Amin, Elizabeth J. Atkinson, Claudie Berger, Lauren A. Burt, Roland Chapurlat, Thierry Chevalley, Serge Ferrari, David Goltzman, David A. Hanley, Marian T. Hannan, Sundeep Khosla, Ching‐Ti Liu, Mattias Lorentzon, Dan Mellström, Blandine Merle, Maria Nethander, René Rizzoli, Elisabeth Sornay‐Rendu, Bert van Rietbergen, Daniel Sundh, Andy Kin On Wong, Claes Ohlsson, Serkalem Demissie, Douglas P. Kiel, Mary Bouxsein

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2018
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity Health NetworkMcMaster UniversityMcGill University Health CentreSt. Joseph’s Healthcare HamiltonUniversity of CalgaryAlberta Bone and Joint Health Institute
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institutes of HealthNational Center for Advancing Translational SciencesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNovo Nordisk Fonden
KeywordsMedicineFemoral neckFRAXOsteoporosisBone mineralHazard ratioCohortBone densityQuantitative computed tomographyHip fractureInternal medicineProspective cohort studyFramingham Risk ScoreProportional hazards modelCohort studySurgeryConfidence intervalOsteoporotic fracture

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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.011
GPT teacher head0.306
Teacher spread0.295 · 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

Citations394
Published2018
Admission routes1
Has abstractno

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