Systematic Review of Incidence Studies of Pneumonia in Persons with Spinal Cord Injury

Pneumonia continues to complicate the course of spinal cord injury (SCI). Currently, clinicians and policy-makers are faced with only limited numbers of pneumonia incidence in the literature. A systematic review of the literature was undertaken to provide an objective synthesis of the evidence about the incidence of pneumonia in persons with SCI. Incidence was calculated per 100 person-days, and meta-regression was used to evaluate the influence of the clinical setting, the level of injury, the use of mechanical ventilation, the presence of tracheostomy, and dysphagia. For the meta-regression we included 19 studies. The incidence ranged from 0.03 to 7.21 patients with pneumonia per 100 days. The main finding of this review is that we found large heterogeneity in the reporting of the incidence, and we therefore should be cautious with interpreting the results. In the multivariable meta-regression, the incidence rate ratios showed very wide confidence intervals, which does not allow a clear conclusion concerning the risk of pneumonia in the different stages after a SCI. Large longitudinal studies with a standardized reporting on risk factors, pneumonia, and detailed time under observation are needed. Nevertheless, this review showed that pneumonia is still a clinically relevant complication and pneumonia prevention should focus on the ICU setting and patients with complete tetraplegia.

Documenting the Need for Patient-Centered Relevant Outcomes in Adult Day Services

ADCs are not uniformly regulated at the federal or state level, resulting in the absence of uniform data collection. The lack of large-scale data has resulted in a dearth of evidence on the role ADC services play in the health and well-being of their clients, particularly persons living with dementia (PLWD). The purpose of this study was to compare data being collected across states and evaluate the degree to which patient centered relevant outcomes (PCROs) are being collected. A review of ADC regulations in 50 states found that <10 states, required standardized reporting on ADC participants. Regulatory forms relied on clinical judgment as opposed to validated tools, and focused on eligibility for services as opposed to independence, engagement, or clinical interventions in the ADC. Emphasizing collection of PCROs in ADCs, beginning at the state level, is an essential step in documenting the value and effectiveness of ADCs, particularly for PLWD.

Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors

The last 2 decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, the air quality monitoring community, and for commercial and private use, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensor systems using reference instrumentation with user-friendly guidelines, open-access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both multiple linear regression (MLR) and the random forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOSs). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensor systems, we suggest a number of best practices for future studies using low-cost sensor systems to ensure greater comparability of research.

Standardized Reporting of Workflow Metrics in Acute Ischemic Stroke Treatment: Why and How?

The benefit of acute ischemic stroke (AIS) treatment is highly time dependent. Although studies on workflow improvement in AIS are increasingly gaining attention, there is a lack of consensus and consistency regarding the definition, measurement, and reporting of AIS workflow times. We discuss the challenges related to defining and measuring workflow times in AIS and propose a basic set of time intervals that should be reported in AIS workflow studies. We particularly focus on patients undergoing mechanical thrombectomy. Importantly, endovascular treatment workflow times should always be reported in conjunction with reperfusion quality because one is not informative without the other. We further suggest standardized reporting of workflow times that includes the 90th percentile in addition to medians and interquartile ranges, means, and SDs. The proposed methodology serves as a framework for AIS studies and aids further discussion on workflow‐related AIS research.

Standardized Reporting Needed to Improve Accuracy of Flaring Data

Gas flaring represents a large waste of a natural resource for energy production and is a significant source of greenhouses gases to the atmosphere. The World Bank estimates annual flared gas volumes of 150 billion cubic meters, based upon a conversion of remotely sensed radiant heat data from the NOAA’s VIIRS (Visible Infrared Imaging Radiometer Suite) instrument onboard the polar-orbiting Suomi NPP satellite. However, the conversion of the remotely sensed radiant heat measurements into flared gas volumes currently depends on flare operator reported volumes, which can be biased in various ways due to inconsistent reporting requirements. Here, I discuss both known and unknown biases in the datasets, using them to illustrate the current lack of accuracy in the widely discussed flaring numbers. While volume trends over time could be derived directly from the radiant heat data, absolute amounts remain questionable. Standardizing how flared gas volumes are measured and reported could dramatically improve accuracy. In addition, I suggest expanding satellite measurements of individual flares burning under controlled conditions as a major improvement to daily monitoring, alongside the potential usage of remotely sensed flare temperature to estimate combustion efficiency.

Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.