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Record W4415438966 · doi:10.1302/1358-992x.2025.10.130

EVALUATION OF A HYBRID DIGITAL AND IN-PERSON OUTPATIENT FRACTURE LIAISON SERVICE FOR NON-HIP FRAGILITY FRACTURES

2025· article· en· W4415438966 on OpenAlexaffabout
P. Schneider, L. Kennedy, Emma O. Billington, Stephanie S. Yee, Peter L. Duffy, Andrew Dodd, Richard M. Martin, R. Buckley, Robert Korley

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOsteoporosisFRAXFragilityFragility fractureHealth careHip fractureOutpatient clinicAmbulatory care

Abstract

fetched live from OpenAlex

Osteoporosis affects over 2.3 million Canadians (1). Employing an osteoporosis nurse coordinator can improve diagnosis and management from 48% to 96% (2). A 3i model of care, which appropriately identifies fragility fractures, organizes investigations, and initiates a bone health care plan has been shown to be cost-effective for hip fracture care (3), yet there is a paucity of 3i fracture liaison programs (FLSs) for non-hip fragility fractures. An opportunity exists to incorporate digital health solutions into the design of outpatient FLSs, in order to increase efficiency and access. This study aimed to quantify recruitment rates, future fracture risk, first line therapy initiation, and patient satisfaction with a 3i outpatient FLS approved by Osteoporosis Canada. This is a prospective cohort study of adult patients (18 years or older) presenting to a Level 1 trauma centre outpatient fracture clinic with a non-hip and non-vertebral, low-energy fracture (i.e., fall from standing height). Once enrolled in the outpatient FLS, the nurse coordinator completed an in-person comprehensive health history and evaluated future fracture risk using the FRAX assessment tool. Based on risk stratification and our established bone health care pathway, the appropriate investigations were completed (i.e., bloodwork, bone mineral density testing). These results and individualized care plans were reviewed with the program orthopaedic surgeon and endocrinologist via teleconference. Personalized care plans and follow-ups were then completed by the research nurse in-person or virtually, based on each patient's preference. Patient satisfaction was evaluated via a survey. Descriptive statistics were used for analysis and all patients had a minimum of six months follow-up. A total of 185 consecutive patients were recruited between October 2020 and May 2022. The majority were female (88.1%), with an average age of 65.9 (± 9.8) years. The most common presenting fracture type was a distal radius fracture, followed by proximal humerus fractures (Figure 1). In total, 33.3% of patients were classified as high-risk, based on a FRAX assessment of a 10-year major osteoporotic fracture probability of 20% or more, while 49.4% were classified as high-risk, based on a FRAX assessment of a 10-year hip fracture probability of 3% or more. Based on Osteoporosis Canada guidelines, osteoporosis therapy was initiated for 47% of patients at high-risk for subsequent fracture. Additionally, 6.5% of patients presented already on pre-injury pharmacotherapy had an alteration in their prescriptions to help optimize their bone health. Alendronate was the most commonly prescribed pharmacotherapy (60%). A total of 98% of participants felt the program should “probably” or “definitely” be continued and 95% rated the program as “good” or “excellent.” Nearly half of the patients presenting to a fracture clinic with a fragility fracture are high-risk for subsequent fracture and meet criteria for an informed discussion about osteoporosis pharmacotherapy initiation for reducing fracture risk. This hybrid model of in-person and virtual care provides increased accessibility to a 3i outpatient FLS for non-hip and non-vertebral body fracture patients. Patient satisfaction was very high and investigation into cost-savings with a hybrid model is warranted. For any figures or tables, please contact the authors directly.

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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.168
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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