Barriers to the identification of fragility fractures for secondary fracture prevention in an orthopaedic clinic-based fracture liaison service: a prospective cohort study
Bibliographic record
Abstract
Background: The goal of this study was to determine the identification and participation rates of fragility fracture patients in a Fracture Liaison Service (FLS). We also identified factors affecting performance in patient identification. Methods: Surgeons, staff, and FLS nurses of an outpatient orthopaedic clinic from a hospital (Montreal, Canada) identified patients 50 yr of age or older with a fragility fracture eligible to join an FLS from January 2014 to March 2015. The list of orthopaedic referrals for the same period was retrieved and compared to our list of patients in the FLS to determine the participation rate. An in-house questionnaire was dispensed to volunteer staff to identify gaps in fragility fracture identification. Results: We identified 1011 patients with fractures from the orthopaedic referral list. Two hundred forty-nine patients (24.6%) were not identified because of nonreferral by surgeons or staff. Of the 762 remaining patients, 288 were excluded for high-energy trauma (n = 126), fracture of the face, skull, foot, or hand (n = 87), and other reasons (n = 75). Out of 474 patients with fragility fracture, 295 (62.2%) joined the FLS (178 refusals (37.6%). FLS managers only accessed 46.9% (474/1011) of eligible patients. The highest difficulty reported by the staff was about the time allocated to patient identification considering their workload. Conclusions: Major barriers to diagnosis and treatment of underlying osteoporosis in fragility fractures are nonreferral from orthopaedic surgeons or staff, and patient refusal. Challenges reside in implementing an institutional policy for optimal screening, better surgeon, staff, and patient education combined with improved systematic clinical management programs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".