No abstract

Introduction:Despite increasing recognition of atypical femoral fractures (AFFs), there’s conflicting evidence about incidence, aetiology, and short-term outcomes of these injuries. This study reports the incidence of AFFs at our center and compares the early postoperative outcomes against typical femoral fractures (TFFs).Methods:A retrospective observational cohort study of patients presenting to our trauma unit between November 2015 and July 2016 was undertaken. Inclusion criteria required radiologically confirmed proximal femoral fracture, which was then categorized as AFF or TFF. Primary outcome measures included length of stay, discharge destination, and 30-day mortality.Results:Two hundred thirty-nine patients presented to our trauma unit over 9 months with either a fractured neck of femur or proximal femoral fracture. A total of 122 were identified as pertrochanteric, subtrochanteric, or proximal femoral shaft fractures of which 25 (20.5%) displayed atypical radiographic features consistent with AFF. The 2 groups were similar for average age (TFF 85.3 years vs AFF 85.0 years), gender (19% vs 16% male gender), American Society of Anaesthesiology grade (3.0 vs 3.0), cognitive score (abbreviated mental test score = 7.03 vs 7.08), and preinjury place of residence (88.9% vs 92.0% lived in own home). Typical fractures were fixed with either dynamic hip screw or intramedullary nailing, all atypical fractures were fixed with intramedullary nailing. There was no statistical difference between the 2 groups for length of stay (12.8 days vs 14.3 days; P > .05), discharge to preinjury residence (45.1% vs 36%; P > .05), or 30-day mortality (8.1% vs 12%; P > .05).Discussion:In our predominantly geriatric population atypical radiographic features were observed in around 10% of patients presenting with proximal femoral fractures or fractured neck of femur. Previous studies have reported poor outcomes for pain, mobility, and length of stay after AFF. However, we observed no difference in short-term outcome measures when compared to patients with typical proximal femoral fracture patterns at our trauma unit.Conclusion:With modern principles of trauma care outcomes achieved following AFFs may be equivalent to typical femoral fractures in the geriatric population.

VeridicalFlow is a Python package that simplifies building reproducible and trustworthy data science pipelines using the PCS (predictability-computability-stability) framework (Yu & Kumbier, 2020). It provides users with a simple interface for stability analysis, i.e., checking the robustness of results from a data science pipeline to various judgement calls made during modeling. This ensures that arbitrary judgement calls made by data practitioners (e.g., specifying a default imputation strategy) do not dramatically alter the final conclusions made in a modeling pipeline. In addition to wrappers facilitating stability analysis, VeridicalFlow also automates many cumbersome coding aspects of Python pipelines, including experiment tracking and saving, parallelization, and caching, all through integrations with existing Python packages. Overall, the package helps to code using the PCS framework by screening models for predictive performance, helping automate computation, and facilitating stability analysis.

No abstract