Fracture liaison services creation is the key to decrease fragility fractures morbidity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".