Gender and Geographic Origin as Determinants of Manuscript Publication Outcomes: JBMR® Bibliometric Analysis from 2017 to 2019.

The Journal of Bone and Mineral Research (JBMR®), the flagship journal of the American Society for Bone and Mineral Research (ASBMR), enjoys a premiere position in its field and has a global reach. The journal uses a single-blind peer-review process whereby three editors are typically involved in assessing each submission for publication, in addition to external reviewers. Although emphasizing fairness, rigor, and transparency, this process is not immune to the influence of unconscious biases. The gender and geographic diversity of JBMR® authors, editors, and reviewers has increased over the last three decades, but whether such diversity has affected peer-review outcomes is unknown. We analyzed manuscript acceptance rates based on the gender and geographic origin of authors, reviewers, and Associate Editors. The analysis included 1662 original research articles submitted to JBMR® from September 2017 through December 2019. Gender was assigned using probabilities from an online tool and manually validated through internet searches. Predictor variables of manuscript outcome were determined with multivariate logistic regression analysis. The acceptance rate was highest when the first and last authors were of different genders, and lowest when both authors were men. Reviewer gender did not influence the outcome regardless of the genders of the first and last authors. Associate Editors from all geographical regions tended to select reviewers from their same region. The acceptance rate was highest when the Associate Editor was from Europe. Manuscripts with authors from North America and Australia/New Zealand had greater overall odds of acceptance than those from Europe and Asia. Manuscripts reviewed only by Editorial Board (EB) members had a lower acceptance rate than those refereed by non-EB reviewers or a mix of EB and non-EB reviewers. Overall, the geographical origin of authors, reviewers, and editors, as well as reviewers' EB membership may influence manuscript decisions. Yet, the JBMR® peer-review process remains largely free from gender bias. © 2022 American Society for Bone and Mineral Research (ASBMR).

Validation of the modified radiographic union score for tibia fractures (mRUST) in murine femoral fractures.

Bony union is a primary predictor of outcome after surgical fixation of long bone fractures. Murine models offer many advantages in assessing bony healing due to their low costs and small size. However, current fracture recovery investigations in mice frequently rely on animal sacrifice and costly analyses. The modified Radiographic Union Score for Tibia fractures (mRUST) scoring system is a validated metric for evaluating bony healing in humans utilizing plain radiographs, which are relatively inexpensive and do not require animal sacrifice. However, its use has not been well established in murine models. The aim of this study was to characterize the longitudinal course of mRUST and compare mRUST to other conventional murine fracture analyses. 158 mice underwent surgically created midshaft femur fractures. Mice were evaluated after fracture creation and at 7, 10, 14, 17, 21, 24, 28, 35, and 42 days post-injury. mRUST scoring of plain radiographs was performed by three orthopaedic surgeons in a randomized, blinded fashion. Interrater correlations were calculated. Micro-computed tomography (µCT) was analyzed for tissue mineral density (TMD), total callus volume (TV), bone volume (BV), trabecular thickness, trabecular number, and trabecular separation. Histomorphometry measures of total callus area, cartilage area, fibrous tissue area, and bone area were performed in a blinded fashion. Ultimate torque, stiffness, toughness, and twist to failure were calculated from torque-twist curves. A sigmoidal log-logistic curve fit was generated for mRUST scores over time which shows mRUST scores of 4 to 6 at 7 days post-injury that improve to plateaus of 14 to 16 by 24 days post-injury. mRUST interrater correlations at each timepoint ranged from 0.51 to 0.86, indicating substantial agreement. mRUST scores correlated well with biomechanical, histomorphometry, and µCT parameters, such as ultimate torque (r=0.46, p<0.0001), manual stiffness (r=0.51, p<0.0001), bone percentage based on histomorphometry (r=0.86, p<0.0001), cartilage percentage (r=-0.87, p<0.0001), tissue mineral density (r=0.83, p<0.0001), BV/TV based on µCT (r=0.65, p<0.0001), and trabecular thickness (r=0.78, p<0.0001), among others. These data demonstrate that mRUST is reliable, trends temporally, and correlates to standard measures of murine fracture healing. Compared to other measures, mRUST is more cost-effective and non-terminal. The mRUST log-logistic curve could be used to characterize differences in fracture healing trajectory between experimental groups, enabling high-throughput analysis.

Predicting fracture healing with blood biomarkers: the potential to assess patient risk of fracture nonunion.

Fracture non-union is a significant orthopaedic problem affecting a substantial number of patients yearly. Treatment of nonunions is devastating to patients and costly to the healthcare system. Unfortunately, the diagnosis of non-union is typically made in a reactionary fashion by an orthopaedic surgeon based on clinical assessment and radiographic features several months into treatment. For this reason, investigators have been trying to develop prediction algorithms; however, these have relied on population-based approaches and lack the predictive capability necessary to make individual treatment decisions. There is also a growing body of literature focussed on identifying blood biomarkers that are associated with non-union. This review describes the research that has been done in this area. Further studies of patient-centered, precision medicine approaches will likely improve fracture non-union diagnostic/prognostic capabilities.