Performance of a Fracture Liaison Service in an Orthopaedic Setting
Bibliographic record
Abstract
BACKGROUND: Many Fracture Liaison Services (FLSs) have been successfully implemented, but very few incorporate systematic longitudinal follow-up. The objective of this study was to report on the performance of such an FLS using key performance indicators and longitudinal clinical outcomes. METHODS: An FLS was implemented in 2 outpatient orthopaedic clinics. Men and women who were ≥40 years of age and had a recent fragility fracture were recruited. Participants were evaluated, treated when appropriate, and systematically followed over a 2-year period. Clinical data including chart review and questionnaires were collected. Medical services and hospitalization claims data were retrieved from administrative databases. The primary outcomes were the following key performance indicators: the numbers of investigated and treated patients, follow-up attendance, and the incidence of subsequent fractures. Secondary outcomes were the changes in bone turnover markers and quality of life, physical capacity, and pain scores between baseline and follow-up visits. RESULTS: A total of 532 subjects with a mean age of 63.4 years were recruited; 85.7% were female. Bone mineral density results were collected for 472 subjects (88.7%) and a prescription for anti-osteoporosis medication was given to 86.6% of patients. Overall, 83.6% of patients attended at least 1 follow-up visit. The subsequent fracture incidence rate was 2.6 per 100 person-years (23 fractures). The mean level of type-I collagen C-telopeptide (CTX-1), a bone resorption marker, decreased >35%. Clinically important improvements of functional capacity scores (by 14.4% to 63.7%) and pain level (by 19.3% to 35.7%) were observed over time; however, the increase in quality-of-life scores was not clinically important (by 3% to 15.2%). CONCLUSIONS: In this FLS, the rates of investigation, treatment, and participation were >80% over a 2-year period. The subsequent fragility fracture incidence rate was <3 per 100 person-years. These results suggest that an intensive FLS model of care, with a systematic longitudinal follow-up, is effective. A randomized controlled trial is needed to support these results. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".