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Record W4390929413 · doi:10.1007/s00198-023-07012-1

A meta-analysis of previous falls and subsequent fracture risk in cohort studies

2024· review· en· W4390929413 on OpenAlexaff
Liesbeth Vandenput, Helena Johansson, Eugène McCloskey, Enwu Liu, Marian Schini, Kristina Åkesson, Fred Anderson, Rafael Azagra, Cecilie L. Bager, Charlotte Beaudart, Heike A. Bischoff‐Ferrari, Emmanuel Biver, Olivier Bruyère, Jane A. Cauley, Roland Chapurlat, Claus Christiansen, Cyrus Cooper, Carolyn Crandall, Steven R. Cummings, José António Pereira da Silva, Bess Dawson‐Hughes, Adolfo Díez‐Pérez, Alyssa B. Dufour, John A. Eisman, Petra J. M. Elders, Serge Ferrari, Yuki Fujita, Saeko Fujiwara, Claus‐Christian Glüer, Inbal Goldshtein, David Goltzman, Vilmundur Guðnason, Didier Hans, Mari Hoff, Rosemary Hollick, Martijn Huisman, Masayuki Iki, Sophia Ish‐Shalom, Graeme Jones, Magnus K. Karlsson, Sundeep Khosla, Douglas P. Kiel, Woon‐Puay Koh, Fjorda Koromani, Mark A. Kotowicz, Heikki Kröger, Timothy Kwok, Olivier Lamy, Arnulf Langhammer, Bagher Larijani, Kurt Lippuner, Fiona E. McGuigan, Dan Mellström, Thomas Merlijn, Tuan V. Nguyen, Anna Nordström, Peter Nordström, Terence W O’Neill, Barbara Obermayer‐Pietsch, Claes Ohlsson, Eric Orwoll, Julie A. Pasco, Fernando Rivadeneira, Anne‐Marie Schott, Eric J. Shiroma, Kristín Siggeirsdóttir, Eleanor M. Simonsick, Elisabeth Sornay‐Rendu, Reijo Sund, Karin M. A. Swart, Paweł Szulc, Junko Tamaki, David Torgerson, Natasja M. van Schoor, Tjeerd van Staa, Joan Vila, Nicholas J. Wareham, Nicole C. Wright, Noriko Yoshimura, MCarola Zillikens, Marta Zwart, Nicholas C. Harvey, Mattias Lorentzon, William D. Leslie, John А. Kanis

Bibliographic record

VenueOsteoporosis International · 2024
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of ManitobaMcGill University Health Centre
FundersNational Heart, Lung, and Blood InstituteNational Center for Advancing Translational SciencesTeva Pharmaceutical IndustriesMedical Research CouncilGilead SciencesServierEuropean Foundation for the Study of DiabetesHartstichtingAlexion PharmaceuticalsAchmeaAmgen AustraliaAgNovos HealthcareNational Institute on AgingNational Institute for Health and Care ResearchAmgenRadius HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesSanofiMereo BioPharmaSociedad Española de Medicina de Familia y ComunitariaPfizerZonMwNational Health and Medical Research CouncilAstraZenecaEli Lilly and CompanyNational Institutes of HealthUCB PharmaArgenxU.S. Department of Health and Human Services
KeywordsMedicineFRAXHip fracturePoisson regressionHazard ratioProspective cohort studyOsteoporosisInternal medicineCohort studyConfidence intervalRisk factorPoison controlBone mineralPhysical therapyOsteoporotic fracturePopulationEmergency medicineEnvironmental health

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)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0160.040
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.461
Teacher spread0.324 · 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 designMeta-analysis
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

Citations62
Published2024
Admission routes1
Has abstractno

Explore more

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