Cost of Healthcare Services in Geriatric Neuro-oncology: A descriptive Analysis.

BACKGROUND: The percentage of brain tumor incidence exceeds 50% in the geriatric population admitted at Khoula Hospital (a tertiary care hospital in Oman) as compared to the younger population, furthermore, geriatric patients impose a higher cost of healthcare in general. Therefore, geriatric tumor care is causing a significant burden on the healthcare service in Oman. For this reason, we have developed this study to identify the cost of care for this group. METHODS: Medical data with their costs were collected retrospectively for 108 patients diagnosed with a brain tumor above the age of 65 years and admitted at Khoula Hospital between 2016 and 2019. RESULTS: The two most common diagnoses in terms of incidence were Meningiomas (31.73%) and Glioblastomas (16.34%). Lymphoma peaked with regards to the cost, with an average cost of 8993.83 USD per diagnosis, followed by glioblastoma and then metastatic lesions (with 5039.18 and 4915.76 USD respectively). Of these surgeries, 82.61% were elective, most of which had a cost above or equal to the average. Emergency surgeries showed equal numbers above and below the average cost of surgeries (P< 0.05). The total costs of imaging were 73759.4 USD, with CT (35.8%) + MRI (32.9%) forming 68.7% of the total imaging costs. While laboratory investigation costs totaled 64110.93 USD. CONCLUSION: The cost of tumor care in Oman is variable based on multiple factors. The reported results represent useful information forming the basis for further analysis such as cost-benefit analysis, cost-effectiveness analysis, and cost-utility analysis.

A dataset of human body tracking of walking actions captured using two Azure Kinect sensors.

A dataset of body tracking information is presented. The dataset consists of 315 captured walking sequences. Each sequence is simultaneously captured by two Azure Kinect devices. The two captures are interleaved to effectively double the frame rate. Fifteen participants partook in this experiment. Each experiment consists of seven walking actions, and having three predefined trajectories per experiment. That results in 21 sequences per participant. The data were collected using the Azure Kinect Sensor SDK. They were later processed using the official tools and libraries provided by Microsoft. For each sequence and trajectory, the positions and orientations of thirty-two tracked joints were obtained and saved. The dataset is structured as follows. The experiments from each subject are saved in a single directory. Each directory contains multiple JSON files of timestamped body tracking information to enable the fusion of the two device streams. A calibration file is also provided, enabling the mapping of the coordinates between the two Azure Kinect devices capturing the data (mapping the coordinates of the device known as the Subordinate device to the Master device coordinate system). This data can be used to train neural networks for human motion prediction tasks or test pre-existing algorithms on Azure Kinect data. This dataset could also aid in gait recognition and analysis, as well as in performing action recognition and other surveillance activities. The dataset can be found at https://zenodo.org/record/7997856.

Extraction Planning in Orthodontics.

INTRODUCTION: Planning extraction of teeth for orthodontic treatment is one of the challenging factors for successful functional and esthetic orthodontic results and for the long-term stability. AIM: The aim of this study is to document possible extraction guidelines for planning of orthodontic treatment for space deficiency cases related to excess tooth material. RESULTS: The guidelines are written as patterns to facilitate the decision of extraction of teeth for class I, class II division 1, class II division 2, and class III malocclusions, for crowding, overjet reduction, or correcting the buccal segment relationship. The decision for extraction of teeth for orthodontic treatment is enforced by the oral hygiene, carious teeth, periodontal involvement, impacted teeth, supernumeraries, hypodontia, or for orthognathic purposes. Also, planning the decision for accurate extraction pattern is the main factor in the success of the orthodontic treatment and for the long-term stability of the results. CONCLUSION: Orthodontic tooth extraction should always be planned with consideration of the width and length of the face, the oral hygiene, carious activity, periodontal involvement, malformed crowns, length and health of the root of the teeth, prognosis of impacted teeth, supernumeraries, and hypodontia. CLINICAL SIGNIFICANCE: Accurate diagnosis and treatment planning following orthodontic extraction guidelines lead to a long-term stability of the corrected results.