Clinical effectiveness of a fracture liaison service for geriatric hip fracture patients in Hong Kong
Bibliographic record
Abstract
Introduction: Fragility hip fracture has become a huge public health burden with ageing population. Yet workup and treatment of osteoporosis following a fragility fracture remain under-utilised in clinical practice. The purpose of this study was to evaluate the clinical effectiveness of Fracture Liaison Service (FLS) in preventing future fractures and mortality rate following hip fracture in Hong Kong. Investigation and treatment rate of osteoporosis were also assessed. Methods: A retrospective single-centre cohort study was conducted to study patients aged over 65 years admitted to our hospital for fragility hip fracture before and after FLS implementation. Patients who were recruited in the FLS programme following hospital admission from October 2020 to September 2021 were regarded as the intervention group, whereas those admitted from April 2019 to March 2020 were regarded as historical control. The follow up time was 15 months for both groups. Results: A total of 139 patients were included in the study, with 68 in the intervention group and 71 in the control group. The refracture rate dropped from 5.6% to 0% after FLS implementation, while the 15-month mortality rate increased slightly from 0% to 2.9%, both of which did not show statistical significance, with a p -value of .120 and .238 respectively. Post-injury bone density investigation rate increased from 8.5% to 60.3%, and antiosteoporotic treatment initiation rate increased from 19.7% to 97.1%. Both of them showed statistical significance ( p < .001). Treatment discontinuation rate decreased from 14.3% to 7.8%, with a p -value of .603. Conclusions: FLS implementation in Hong Kong was an effective tool to increase investigation and treatment rate of osteoporosis in geriatric hip fracture patients. Its impact on refracture and mortality rate remained insignificant in this cohort of patients. Further study with a longer follow up time and more delicate selection of participants may demonstrate a better outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".