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Record W4388627289 · doi:10.1007/s00198-023-06955-9

Patients’ preferences for fracture risk communication: the Risk Communication in Osteoporosis (RICO) study

2023· article· en· W4388627289 on OpenAlexafffundabout
Charlotte Beaudart, Mitali Sharma, Patricia Clark, Saeko Fujiwara, Jonathan D. Adachi, Osvaldo Daniel Messina, Suzanne N. Morin, Lynn Kohlmeier, Caroline B. Sangan, Xavier Nogués, Griselda Adriana Cruz-Priego, Andrea Cavallo, Fiona Cooper, Jamie Grier, Carolyn Leckie, Diana Montiel-Ojeda, Αλεξάνδρα Παπαϊωάννου, Nele Raskin, Leonardo Yurquina, Michelle Wall, Olivier Bruyère, Annelies Boonen, Elaine Dennison, Nicholas C. Harvey, John А. Kanis, Jean‐François Kaux, E. Michael Lewiecki, Oscar Lopez-Borbon, Zoé Paskins, Jean‐Yves Reginster, Stuart L. Silverman, Mickaël Hiligsmann

Bibliographic record

VenueOsteoporosis International · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcGill University Health CentreMcGill UniversityMcMaster University
FundersMedical Research CouncilVersus ArthritisNational Institute for Health and Care ResearchDepartment of Health and Social CareOsteoporosis CanadaAmgen
KeywordsMedicineOsteoporosisRheumatologyRisk communicationInternal medicineOrthopedic surgeryFamily medicineSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

The RICO study indicated that most patients would like to receive information regarding their fracture risk but that only a small majority have actually received it. Patients globally preferred a visual presentation of fracture risk and were interested in an online tool showing the risk. PURPOSE: The aim of the Risk Communication in Osteoporosis (RICO) study was to assess patients' preferences regarding fracture risk communication. METHODS: To assess patients' preferences for fracture risk communication, structured interviews with women with osteoporosis or who were at risk for fracture were conducted in 11 sites around the world, namely in Argentina, Belgium, Canada at Hamilton and with participants from the Osteoporosis Canada Canadian Osteoporosis Patient Network (COPN), Japan, Mexico, Spain, the Netherlands, the UK, and the USA in California and Washington state. The interviews used to collect data were designed on the basis of a systematic review and a qualitative pilot study involving 26 participants at risk of fracture. RESULTS: A total of 332 women (mean age 67.5 ± 8.0 years, 48% with a history of fracture) were included in the study. Although the participants considered it important to receive information about their fracture risk (mean importance of 6.2 ± 1.4 on a 7-point Likert scale), only 56% (i.e. 185/332) had already received such information. Globally, participants preferred a visual presentation with a traffic-light type of coloured graph of their FRAX® fracture risk probability, compared to a verbal or written presentation. Almost all participants considered it important to discuss their fracture risk and the consequences of fractures with their healthcare professionals in addition to receiving information in a printed format or access to an online website showing their fracture risk. CONCLUSIONS: There is a significant communication gap between healthcare professionals and patients when discussing osteoporosis fracture risk. The RICO study provides insight into preferred approaches to rectify this communication gap.

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)0.009
metaresearch head score (Gemma)0.021
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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.122
GPT teacher head0.406
Teacher spread0.285 · 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

Citations13
Published2023
Admission routes3
Has abstractyes

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