Engaging health care providers to improve the referral and evaluation processes for potential transplant candidates--The Toronto General Hospital Experience.

The Kidney Transplant Program (KTP) at the Toronto General Hospital has taken great strides in preparing to meet the needs of patients and health care providers, as the number of end-stage renal disease patients in Ontario increases. The KTP has begun the process of increasing engagement and collaboration with various stakeholders from the pre- to the post-transplant phase through (1) the development of innovative programs to increase the number of live kidney donations, (2) the development and maintenance of information technology solutions that work simultaneously to provide data to manage and treat patients, and conduct research, and (3) the development, implementation, and delivery of educational presentations and tools to various stakeholders both at the referring centres and the transplant program. Future steps for the KTP include evaluating the impact of these programmatic tools and activities on the number of referrals received and the subsequent effect on the number of transplants performed.

Health information management for research and quality assurance: the Comprehensive Renal Transplant Research Information System.

The Kidney Transplant Program at the Toronto General Hospital uses numerous electronic health record platforms housing patient health information that is often not coded in a systematic manner to facilitate quality assurance and research. To address this, the comprehensive renal transplant research information system was conceived by a multidisciplinary healthcare team. Data analysis from comprehensive renal transplant research information system presented at programmatic retreats, scientific meetings, and peer-reviewed manuscripts contributes to quality improvement and knowledge in kidney transplantation.

Performance measures for the evaluation of patients referred to the Toronto General Hospital's kidney transplant program.

Given the increasing number of patients with end-stage renal disease in Ontario, there is a need to improve the efficiency and effectiveness of the pretransplant evaluation, to allow for a seamless progression through the various steps in the process. Toronto General Hospital's kidney transplant program is evaluating various performance measures, specifically looking at waiting times from referral to initial evaluation and initial evaluation to final disposition, to use as metrics for monitoring program performance and stimulate quality improvement.

Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour.

Cannabis flower odour is an important aspect of product quality as it impacts the sensory experience when administered, which can affect therapeutic outcomes in paediatric patient populations who may reject unpalatable products. However, the cannabis industry has a reputation for having products with inconsistent odour descriptions and misattributed strain names due to the costly and laborious nature of sensory testing. Herein, we evaluate the potential of using odour vector modelling for predicting the odour intensity of cannabis products. Odour vector modelling is proposed as a process for transforming routinely produced volatile profiles into odour intensity (OI) profiles which are hypothesised to be more informative to the overall product odour (sensory descriptor; SD). However, the calculation of OI requires compound odour detection thresholds (ODT), which are not available for many of the compounds present in natural volatile profiles. Accordingly, to apply the odour vector modelling process to cannabis, a QSPR statistical model was first produced to predict ODT from physicochemical properties. The model presented herein was produced by polynomial regression with 10-fold cross-validation from 1,274 median ODT values to produce a model with R2 = 0.6892 and a 10-fold R2 = 0.6484. This model was then applied to terpenes which lacked experimentally determined ODT values to facilitate vector modelling of cannabis OI profiles. Logistic regression and k-means unsupervised cluster analysis was applied to both the raw terpene data and the transformed OI profiles to predict the SD of 265 cannabis samples and the accuracy of the predictions across the two datasets was compared. Out of the 13 SD categories modelled, OI profiles performed equally well or better than the volatile profiles for 11 of the SD, and across all SD the OI data was on average 21.9% more accurate (p = 0.031). The work herein is the first example of the application of odour vector modelling to complex volatile profiles of natural products and demonstrates the utility of OI profiles for the prediction of cannabis odour. These findings advance both the understanding of the odour modelling process which has previously only been applied to simple mixtures, and the cannabis industry which can utilise this process for more accurate prediction of cannabis odour and thereby reduce unpleasant patient experiences.

Interobserver variability in the assessment of CT imaging features of traumatic brain injury.

The goal of our study was to determine the interobserver variability between observers with different backgrounds and experience when interpreting computed tomography (CT) imaging features of traumatic brain injury (TBI). We retrospectively identified a consecutive series of 50 adult patients admitted at our institution with a suspicion of TBI, and displaying a Glasgow Coma Scale score < or =12. Noncontrast CT (NCT) studies were anonymized and sent to five reviewers with different backgrounds and levels of experience, who independently reviewed each NCT scan. Each reviewer assessed multiple CT imaging features of TBI and assigned every NCT scan a Marshall and a Rotterdam grading score. The interobserver agreement and coefficient of variation were calculated for individual CT imaging features of TBI as well as for the two scores. Our results indicated that the imaging review by both neuroradiologists and neurosurgeons were consistent with each other. The kappa coefficient of agreement for all CT characteristics showed no significant difference in interpretation between the neurosurgeons and neuroradiologists. The average Bland and Altman coefficients of variation for the Marshall and Rotterdam classification systems were 12.7% and 21.9%, respectively, which indicates acceptable agreement among all five reviewers. In conclusion, there is good interobserver reproducibility between neuroradiologists and neurosurgeons in the interpretation of CT imaging features of TBI and calculation of Marshall and Rotterdam scores.