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Record W4360913912 · doi:10.14341/osteo12962

Fracture liaison services creation is the key to decrease fragility fractures morbidity

2023· article· en· W4360913912 on OpenAlexaff
A. Yu. Kochish, Sarkis Sahakyan

Bibliographic record

VenueOsteoporosis and Bone Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsNational Defence Medical Centre
Fundersnot available
KeywordsMedicineIncidence (geometry)OsteoporosisFRAXBone healingInternal medicineFragilityPhysical therapyDentistrySurgeryBone mineralOsteoporotic fracture

Abstract

fetched live from OpenAlex

Background . Fragility fractures (FF) determine main severity of medical and social consequences of osteoporosis. FF prophylaxis considers to prevent repeated fractures. For this reason special services are being created. Their principles and effectiveness require improvement and additional study. Aim . Тo highlight key role of Fracture Liaison Services (FLS) for reducing incidence of FF by means of scientific publications and own material analysis. Materials and methods . Clinical material was collected at the Artashat Medical Center (Republic of Armenia). Study included 2,332 patients aged 50–97 years, including 1,656 women (71%) and 676 men (29%) with typical FF of limb bones. First group included 765 patients treated in 2011–2013, for whom anti-osteoporotic pharmacotherapy (AP) was prescribed due to known X-ray techniques. In second group of 1567 patients the AP was prescribed based on electronic FRAX calculator in 2014–2017. Proportions of patients in two clinical groups with and without AP were compares along with proportions who had repeated low-energy bone fractures. Moreover, past 20 years scientific publications analysis dedicated to prevention of FF and role of FLS was carried out. Results . Comparative analysis revealed that patients proportion with prescribed AF based on use of FRAX calculator in second clinical group, significantly (p<0.05) increased by 16.6%, while patients proportion with repeated FF significantly (p<0.05) decreased by 6.5% (or 1.17 times) in relation to first group. Analysis of publications has shown high efficiency of FLS and their important role in reducing incidence of FF in general. Conclusion . Use of FRAX calculator for AP prescription allowed to achieve significant (p<0.05) increase by 16.6% in proportion with prescribed specific AP, which led to significant (p<0.05) decrease of patient proportion (6.5%) with repeated low-energy fractures. Analysis of scientific publications and own material confirmed key role of FLS for reducing the incidence of FF.

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.338
Teacher spread0.318 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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