Establishing a hospital based fracture liaison service to prevent secondary insufficiency fractures
Bibliographic record
Abstract
In the aging population worldwide, osteoporosis is a relatively common condition and a major cause of long-term morbidity. Initial fragility fractures can lead to subsequent fractures. After a vertebral fracture, the risk of any another fracture increases 200% and that of a subsequent hip fracture increases 300%. For starting a hospital based Fracture Liaison Service (FLS) program, the nucleus is based on a physician champion, a FLS coordinator, and a nurse manager. A Fracture Liaison Service (FLS) is a multidisciplinary system approach to reducing subsequent fracture risk in patients with a recent fragility fracture due to compromised bone health by identifying them at or close to the time when they are treated at the hospital for fracture and providing them with easy access to osteoporosis care. It has been shown that when compared to other models such as referral letters to primary care physicians or endocrinologists, the FLS model results in a higher rate of diagnosis and treatment with less attrition in the posffracture phase. Insufficiency fracture care requires more than surgery to stabilize a fractured bone. The FLS program provides an opportunity to treat osteoporosis from a public health perspective rather than leaving this to the whims of individual physicians. This is achieved by providing a seamless integration of care by health care providers, nursing staff and administration. The FLS can be adapted to any model of care including academic health systems. FLS provides a holistic approach to identify patients as well as to provide evidence-based interventions to prevent subsequent fractures. The long term goal is that internationally FLS will result in in decreased fracture-related morbidity, mortality and overall health care expenditure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".