Impact of academic collaboration and quality of clinical orthopaedic research conducted in low- and middle-income countries.

BACKGROUND: Little is known about the quality of orthopaedic investigations conducted in low- and middle-income countries (LMICs). Academic collaboration is one model to build research capacity and improve research quality. Our study aimed to determine (1) the quality of clinical orthopaedic research conducted in LMICs, (2) the World Bank Regions and LMICs that publish the highest quality studies, (3) the pattern of collaboration among investigators and (4) whether academic collaboration between LMIC and non-LMIC investigators is associated with studies that have higher levels of evidence. METHODS: Orthopaedic studies from 2004 to 2014 conducted in LMICs were extracted from multiple electronic databases. The World Bank Region, level of evidence and author country-affiliation were recorded. Collaboration was defined as a study that included an LMIC with non-LMIC investigator. RESULTS: There were 958 studies that met inclusion criteria of 22,714 searched. Ninety-seven (10.1%) of included studies achieved Level 1 or 2 evidence, but case series (52.3%) were the most common. Collaboration occurred in 14.4% of studies and the vast majority of these (88.4%) were among academic institutions. Collaborative studies were more likely to be Level 1 or 2 (20.3% vs. 8.4%, p < 0.01), prospective (34.8% vs. 22.9% p = 0.04) and controlled (29.7% vs. 14.4%, p < 0.01) compared to non-collaborative studies. CONCLUSIONS: Although orthopaedic studies in LMICs rarely reach Level 1 or 2 evidence, studies published through academic collaboration between LMIC and non-LMIC investigators are associated with higher levels of evidence and more prospective, controlled designs.

Influence of Weight Loss Attempts on Bariatric Surgery Outcomes.

Many payors require an additional attempt at nonsurgical weight loss before approval of bariatric procedures. This study evaluates this requirement by characterizing the prior weight loss attempts (WLAs) undergone by bariatric surgery patients and correlating those attempts to postoperative weight loss outcomes. Number and duration of WLAs were obtained from a preoperative clinic assessment. Body mass index (BMI) and percentage of excess weight loss (%EWL) were used to assess weight loss. Kruskal-Wallis and Spearman Correlation tests were performed to analyze data using GraphPad Prism 6. Mean number of WLAs before surgery was 3.5 ± 0.2 attempts, with an average duration of 15.2 ± 1.1 years. There was a significant negative correlation between duration of WLAs and preoperative BMI (r = -0.2637, P = 0.0025). No significant difference was found for preoperative BMI or mean 12-month %EWL among any WLA groups. The number and duration of dietary attempts before surgery do not significantly affect long-term weight loss outcomes after bariatric surgery. Given these data, an additional preoperative WLA may not be efficacious in improving patients' chances at weight loss.

Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography.

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.