A multicenter, prospective cohort study conducted in 5 physiotherapy clinics in the UAE from January 2021 to March 2023 to assess coronal lumbar spine radiographic parameters as a predictor of conservative therapy outcomes in patients suffering from low back and leg pain due to lumbar disc herniation (HNP). Ninety patients (mean age 44 yrs., 54% male) with lumbar HNP underwent conservative therapy. All participants received lumbar spine MRI and radiography to assess spine alignment. Interventions included specific exercises, diathermy, traction, education, a home-based exercise pro-gram, and medications. Detailed demographic data was collected. Follow-up was 6-months after discharge. A successful outcome was based on a minimum of the following four outcomes: (1) reduction of pain by 17.5 points (0–100 NRS); (2) fatigue reduction by 7.5 points; (3) distress reduction by 5 points; and (4) interference reduction by 9.5 points. At 6-month follow-up it was found that patient age, education, and radio-graphic determined lumbosacral angle measures significantly affected the odds of a successful outcome. Increasing age 1-year significantly decreased the odds of success of improving pain, fatigue (OR = 0.85, p = 0.016) and interference scores (OR = 0.89, p = 0.042) by 15%, 15%, and 11% respectively. Lower education significantly increased the odds of success for improving pain, fatigue, and interference by 26.18, 26.18, and 7.5 (p = 0.006, = 0.006, and = 0.029 respectively). Increasing (worsening) the radiographic lumbosacral angle by each 1° significantly reduced the odds of success for improving pain, fatigue, distress, and interference by 3.52, 3.52, 27.99, and 2.55, respectively (p < 0.001, < 0.001, = 0.003, = 0.001). Our findings indicate that younger age, less education, and better coronal radiological lumbar spine alignment all had a substantial impact on the likelihood of success of 6-month outcomes in patients suffering from chronic lower back pain and radiculopathy due to HNP.

Objectives: There are limited data regarding the efficacy of methylprednisolone in patients with acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) requiring invasive mechanical ventilation. We aimed to determine whether methylprednisolone increases the number of ventilator-free days (VFDs) among these patients. Design: Retrospective single-center study Setting: Intensive care unit Patients: All patients with ARDS due to confirmed SARS-CoV-2 infection and requiring invasive mechanical ventilation between 1 March and 29 May 2020 were included Interventions: None Measurements and Main Results: The primary outcome was ventilator-free days (VFDs) during the first 28 days, defined as being alive and free from mechanical ventilation. The primary outcome was analyzed with competing-risks regression based on Fine and Gray’s proportional subhazards model. Death before day 28 was considered to be the competing event. A total of 77 patients met the inclusion criteria. Thirty-two patients (41.6%) received methylprednisolone. The median dose was 1 mg.kg-1 (IQR: 1-1.3 mg.kg-1) and median duration of 5 days (IQR:5-7 days). Patients who received methylprednisolone had a mean 18.8 VFDs (95% CI, 16.6-20.9) during the first 28 days vs. 14.2 VFDs (95% CI, 12.6-16.7) in patients who did not receive methylprednisolone (difference, 4.61; 95% CI, 1.10-8.12; P = 0.001). In the multivariable competing-risks regression analysis and after adjusting for potential confounders (ventilator settings, prone position, organ failure support, severity of the disease, tocilizumab, and inflammatory markers), methylprednisolone was independently associated with a higher number of VFDs (subhazards ratio: 0.10, 95%CI: 0.02-0.45; p=0.003). Hospital mortality did not differ between the two groups (31.2% vs. 28.9%, p=0.82). Hospital length of stay was significantly shorter in the methylprednisolone group (24 days [IQR:15-41 days] vs. 37 days [IQR:23-52 days], p=0.046). The incidence of positive blood cultures was higher in patients who received methylprednisolone (37.5% vs. 17.8%, p=0.052). However, 91% of patients who received methylprednisolone also received tocilizumab. The number of days with hyperglycemia was similar in the two groups. Conclusions: Methylprednisolone was independently associated with increased VFDs and shortened hospital length of stay. The combination of methylprednisolone and tocilizumab was associated with a higher rate of positive blood cultures. Further trials are needed to evaluate the benefits and safety of methylprednisolone in moderate or severe COVID-19 ARDS.

Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.