Effect of Ondansetron on Postoperative Pain and Vomiting after Acute Appendicitis Surgery: A Placebo-Controlled Double-Blinded Randomized Clinical Trial.

Background: Common postoperative complications following surgery, particularly acute appendicitis surgery, include postoperative pain and vomiting, which can cause discomfort and delay recovery time. Methods: A randomized double-blinded placebo-controlled clinical trial was conducted with 80 cases of acute appendicitis of American Society of Anesthesiologists (ASA) physical status I or II and aged 18-60 y/o scheduled for appendectomy under general anesthesia. Patients were randomly divided into two equal groups: group A received 4 mg of ondansetron IV (2 ml) and group B received 2 ml of normal slain IV (placebo). Pain according to VAS, nausea and vomiting according to clinical symptoms, shivering and sedation according to the Bedside Shivering Assessment Scale (BSAS), and the Ramsay Sedation Scale (RSS) at 2, 6, 12, and 24 hours after surgery were evaluated and compared between the groups. Results: There was a significant decline in the severity of pain only at 2 hours after surgery between the ondansetron and control groups (5.3 ± 1.0 vs. 6.0 ± 1.0; p=0.01), not showing a difference between the groups at 6, 12, and 24 hours after appendectomy. Postoperative nausea and vomiting at 2 (5% vs. 25%; p=0.03) and 6 (7.5% vs. 27.5%; p=0.04) hours after appendectomy in the ondansetron group. At different times, the ondansetron and control groups did not differ in terms of pethidine consumption or sedation. Conclusions: In conclusion, our study found that ondansetron was effective in reducing postoperative vomiting after acute appendicitis surgery. However, it did not show a clinically significant effect on postoperative pain. This trial is registered with IRCT20230722058883N1.

Concurrent Learning Approach for Estimation of Pelvic Tilt from Anterior-Posterior Radiograph.

Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior-posterior (AP) radiography image. We introduce an encoder-decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks.