Does Fracture Liaison Service Improves Fracture Risk Assessment among Patients with Fragility Fractures? A Systematic Review
Bibliographic record
Abstract
Abstract Background: The fragility fractures can cause substantial pain, disability, reduced quality of life and mortality. The probability of sustaining subsequent fractures increases up to five times after an initial fragility fracture. The Fracture Liaison Service is a coordinated model of care that aims to bridge the post-fracture care gap by improving subsequent fracture risk assessment and post-fracture management. However, there are very few studies that included fracture risk assessment as a significant outcome of an FLS program. This systematic review aims to evaluate the available evidence on the effect of FLS in improving fracture risk assessment among fragility fracture patients Method: A systematic literature search will be carried out on the major electronic databases including PubMed, Embase, CINAHL Plus, and Cochrane to identify the outcomes of Fracture Liaison Service. The literature search will not be restricted to the context and year of publication. Two researchers will independently conduct the databases search. We will pilot the search strategy to ensure sufficient sensitivity and specificity. The JBI critical appraisal tools will be used to assess methodological quality of all the included studies. Discussion: This review will highlight an urgent need for more studies from different geographical areas to determine best practices for implementing fracture risk assessment globally and guiding clinical decision making for osteoporosis management. The findings of this systematic review will highlight the importance of including fracture risk assessment as a significant parameter to evaluate FLS programs implemented across the globe. Conclusion: This systematic review will provide more information about fracture risk assessments and its reporting. It will also highlight the variations in the methods of performing a fracture risk assessment with and without BMD testing and the impact of the FLS program in improving fracture risk assessment.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".