Agreement between physicians’ and nurses’ clinical decisions for the management of the fracture liaison service (4iFLS): the Lucky Bone™ program
Bibliographic record
Abstract
UNLABELLED: We determined if nurses can manage osteoporotic fractures in a fracture liaison service by asking a rheumatologist and an internist to assess their clinical decisions. Experts agreed on more than 94 % of all nurses' actions for 525 fragility fracture patients, showing that their management is efficient and safe. INTRODUCTION: A major care gap exists in the investigation of bone fragility and initiation of treatment for individuals who have sustained a fragility fracture. The implementation of a fracture liaison service (FLS) managed by nurses could be the key in resolving this problem. The aim of this project was to obtain agreement between physicians' and nurses' clinical decisions and evaluate if the algorithm of care is efficient and reliable for the management of a FLS. METHODS: Clinical decisions of nurses for 525 subjects in a fracture liaison service between 2010 and 2013 were assessed by two independent physicians with expertise in osteoporosis treatment. RESULTS: Nurses succeeded in identifying all patients at risk and needed to refer 27 % of patients to an MD. Thereby, they managed autonomously 73 % of fragility fracture patients. No needless referrals were made according to assessing physicians. Agreement between each evaluator and nurses was of >97 %. Physicians' decisions were the same in >96 %, and Gwet AC11 coefficient was of >0.960 (almost perfect level of agreement). All major comorbidities were adequately managed. CONCLUSIONS: High agreement between nurses' and physicians' clinical decisions indicate that the independent management by nurses of a fracture liaison service is safe and should strongly be recommended in the care of patients with a fragility fracture. This kind of intervention could help resolve the existing care gap in bone fragility care as well as the societal economic burden associated with prevention and treatment of fragility fractures.
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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".