The impact of a standardized order set for the management of non-hip fragility fractures in a Fracture Liaison Service
Bibliographic record
Abstract
We analysed the impact of a standardized order set empowering staff nurses to independently manage a Fracture Liaison Service over a 9-month period. Nurses identified between 30 and 70 % of non-hip fragility fractures to the unit in charge of management over time. The latter managed 58 % of referred patients. INTRODUCTION: The main goal of this study was to evaluate the impact of a standardized order set empowering nurses to independently manage a fracture liaison service (FLS). METHODS: Since November 2014, an order set allowed nurses of a Montreal hospital, Quebec, Canada to entirely manage an FLS on their own. Nurses followed an 6-h training program on-site. Emergency department (ED) and orthopaedic outpatient clinic (OC) nurses identified non-hip fragility fractures. Medical day treatment unit (MDTU) nurses were in charge of the management (investigation and treatment initiation). The list of patients, 50 years and older, with a fracture were retrieved for the period of November 2014 to July 2015. Performance was assessed with the rate of identification over time and the rate of management of non-hip fragility fractures. RESULTS: Over the 9-month period, 346 patients of ≥50 years old were seen for a fracture, of which 190 met fragility criteria (excluding hip fractures). A sinusoid pattern of rates of identification between 30-70 % was observed over time. An average proportion of 58.1 % of fracture patients were managed by MDTU nurses. CONCLUSIONS: A standardized order set legally allowing nurses to manage an FLS led to identification rates varying from 30-70 % and a management rate close to 60 % for referred patients over a 9-month period, which largely exceeds that of standard care. Identification was mostly compromised by difficulty integrating the order set into routine practice. Enforcement of the hospital policy on fragility fractures could help yield efficiency of identification of osteoporosis-related fractures by the staff.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".