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Record W4320029756 · doi:10.14740/jcs460

Cost Analysis of a Fracture Liaison Service: A Prospective Study for Secondary Prevention After Fractures of the Hip

2022· article· en· W4320029756 on OpenAlexvenueno aff
Gershon Zinger, Amit Davidson, Noa Sylvetsky, Yedin Levy, Amos Peyser

Bibliographic record

VenueJournal of Current Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoporosisHip fractureRandomized controlled trialProspective cohort studyZoledronic acidCost analysisFragilityPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Fracture liaison services (FLSs) have proven to be effective in treating osteoporosis associated with fragility fractures. For patients with fragility fractures of the hip, FLS programs are expected to be cost-effective because of the high risk of re-fracture and the high cost of fracture treatment. In this study, we evaluated the essential factors in determining whether the FLS saves or loses more than it costs. Methods: A prospective-randomized study was done in patients with hip fragility fractures using a hospital-based FLS program in parallel with a cost analysis. Data were generated from a cohort of patients using actual data for FLS effectiveness, individual costs of hip fracture treatment, and medication costs based on an accepted treatment algorithm. Results: There were 200 patients randomized and 180 analyzed for costs. Results showed that the cost-benefit of the FLS was dependent on the medication used for osteoporosis. Specifically, using the medication algorithm in this study, the loss per patient enrolled in the FLS was $671 for a 2-year period. If intravenous zoledronic acid had been used, then the loss would have been $221. If only oral bisphosphonates had been used, then the FLS would have saved $109 per patient for a 2-year period. Conclusions: The analysis done here shows that medication cost is the critical component in cost-effectiveness of an FLS program. Additional work needs to be done refining the medication algorithm considering medication costs but individualized to patient needs based on fracture risk. J Curr Surg. 2022;12(2):29-37 doi: https://doi.org/10.14740/jcs460

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)0.000
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 score0.018
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.365
Teacher spread0.323 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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