Background: The purpose of this study was to perform a cross-sectional analysis on the gender composition of practicing academic orthopaedic surgeons using three databases composed of clinical orthopaedic surgeons. Methods: A comprehensive database of 4,519 clinically active academic orthopaedic surgeons was compiled for this study after the review of publicly available data. The complied data set was evaluated alongside orthopaedic databases obtained from the 2017 Association of American Medical Colleges (AAMC) Faculty Administrative Management Online User System and the 2016 American Academy of Orthopaedic Surgery (AAOS) Orthopaedic Practice in the US census representing the entire academy membership orthopaedic workforce. Gender status was obtained and compared between the three databases. Results: Of the 4,519 clinically active academic orthopaedic surgeons analyzed, 435 (10%) were female compared with 19% for the AAMC faculty database and 7% for the AAOS members. Fourteen percent of women had achieved the rank of professor compared with 25% of the men ( P < 0.001). AAMC faculty had a significantly higher percentages of female representation compared with both the clinical faculty (19% versus 10%; P < 0.001) and AAOS members (19% versus 7%; P < 0.001). Conclusion: Despite multiple initiatives designed to increase diversity, the 7% female representation in the orthopaedic workforce identified in this study remains markedly lower than other medical and surgical specialties. A higher percentage of women were associated with the AAMC orthopaedic faculty compared with clinically active female surgeons at these institutions. Academic orthopaedic surgeons had greater female representation than the general orthopaedic workforce, highlighting the importance of training institutions at promoting gender equity.

Enhancing LRP5 signaling and inhibiting TGFβ signaling have each been reported to increase bone mass and improve bone strength in wild-type mice. Monotherapy targeting LRP5 signaling, or TGFβ signaling, also improved bone properties in mouse models of Osteogenesis Imperfecta (OI). We investigated whether additive or synergistic increases in bone properties would be attained if enhanced LRP5 signaling was combined with TGFβ inhibition. We crossed an Lrp5 high bone mass (HBM) allele (Lrp5 A214V ) into the Col1a2 G610C/+ mouse model of OI. At 6weeks-of-age we began treating mice with an antibody that inhibits TGFβ1, β2, and β3 (mAb 1D11), or with an isotype-matched control antibody (mAb 13C4). At 12-weeks-old, we observed that combining enhanced LRP5 signaling with inhibited TGFβ signaling produced an additive effect on femoral and vertebral trabecular bone volumes, but not on cortical bone volumes. Although enhanced LRP5 signaling increased femur strength in a 3-point bending assay in Col1a2 G610C/+ mice, femur strength did not improve further with TGFβ inhibition. Neither enhanced LRP5 signaling nor TGFβ inhibition, alone or in combination, improved femur 3-pointbending post-yield displacement in Col1a2 G610C/+ mice. These pre-clinical studies indicate combination therapies that target LRP5 and TGFβ signaling should increase trabecular bone mass in patients with OI more than targeting either signaling pathway alone. Whether additive increases *

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One of the key roles of an orthopedic surgeon treating metastatic bone disease (MBD) is fracture risk prediction. Current widely used impending fracture risk tools such as Mirels scoring lack specificity. Two newer methods of fracture risk prediction, CT-based structural rigidity analysis (CTRA) and finite element analysis (FEA), have each been shown to be more accurate than Mirels. This case series illustrates comparative Mirels, CTRA, and FEA for 8 femurs in 7 subjects. These cases were selected from a much larger data set to portray examples of true positives, true negatives, false positives, and false negatives as defined by CTRA relative to the fracture outcome. Case illustrations demonstrate comparative Mirels and FEA. This series illustrates the use, efficacy, and limitations of these tools. As all current tools have limitations, further work is needed in refining and developing fracture risk prediction.

Background Correctly identifying patients at risk of femoral fracture due to metastatic bone disease remains a clinical challenge. Mirels criteria remains the most widely referenced method with the advantage of being easily calculated but it suffers from poor specificity. The purpose of this study was to develop and evaluate a modified Mirels scoring system through scoring modification of the original Mirels location component within the proximal femur. Methods Computational (finite element) experiments were performed to quantify strength reduction in the proximal femur caused by simulated lytic lesions at defined locations. Virtual spherical defects representing lytic lesions were placed at 32 defined locations based on axial (4 axial positions: neck, intertrochanteric, subtrochanteric or diaphyseal) and circumferential (8 circumferential: 45-degree intervals) positions. Finite element meshes were created, material property assignment was based on CT mineral density, and femoral head/greater trochanter loading consistent with stair ascent was applied. The strength of each femur with a simulated lesion divided by the strength of the intact femur was used to calculate the Location-Based Strength Fraction (LBSF). A modified Mirels location score was next defined for each of the 32 lesion locations with an assignment of 1 (LBSF > 75%), 2 (LBSF: 51–75%), and 3 (LBSF: 0–50%). To test the new scoring system, data from 48 patients with metastatic disease to the femur, previously enrolled in a Musculoskeletal Tumor Society (MSTS) cross-sectional study was used. The lesion location was identified for each case based on axial and circumferential location from the CT images and assigned an original (2 or 3) and modified (1,2, or 3) Mirels location score. The total score for each was then calculated. Eight patients had a fracture of the femur and 40 did not over a 4-month follow-up period. Logistic regression and decision curve analysis were used to explore relationships between clinical outcome (Fracture/No Fracture) and the two Mirels scoring methods. Results The location-based strength fraction (LBSF) was lowest for lesions in the subtrochanteric and diaphyseal regions on the lateral side of the femur; lesions in these regions would be at greatest risk of fracture. Neck lesions located at the anterior and antero-medial positions were at the lowest risk of fracture. When grouped, neck lesions had the highest LBSF (83%), followed by intertrochanteric (72%), with subtrochanteric (50%) and diaphyseal lesions (49%) having the lowest LBSF. There was a significant difference (p < 0.0001) in LBSF between each axial location, except subtrochanteric and diaphyseal which were not different from each other (p = 0.96). The area under the receiver operator characteristic (ROC) curve using logistic regression was greatest for modified Mirels Score using site specific location of the lesion (Modified Mirels-ss, AUC = 0.950), followed by a modified Mirels Score using axial location of lesion (Modified Mirels-ax, AUC = 0.941). Both were an improvement over the original Mirels score (AUC = 0.853). Decision curve analysis was used to quantify the relative risks of identifying patients that would fracture (TP, true positives) and those erroneously predicted to fracture (FP, false positives) for the original and modified Mirels scoring systems. The net benefit of the scoring system weighed the benefits (TP) and harms (FP) on the same scale. At a threshold probability of fracture of 10%, use of the modified Mirels scoring reduced the number of false positives by 17–20% compared to Mirels scoring. Conclusions A modified Mirels scoring system, informed by detailed analysis of the influence of lesion location, improved the ability to predict impending pathological fractures of the proximal femur for patients with metastatic bone disease. Decision curve analysis is a useful tool to weigh costs and benefits concerning fracture risk and could be combined with other patient/clinical factors that contribute to clinical decision making.