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While evidence supports the use of cadavers to facilitate the teaching and learning of human anatomy, cadaver-based teaching may not be present at the undergraduate level at many institutions due to limited laboratory access, financial constraints, and the lack of qualified faculty trained to teach in this type of setting. The following case example outlines a unique program designed to provide cadaver-based instruction to undergraduate students, while simultaneously training undergraduate faculty to teach in this setting through peer observational methods. More specifically, the following teaching collaboration was designed with the intent to achieve the following: (1) expose undergraduate anatomy students to cadaver-based learning; (2) provide education and training to undergraduate faculty so they are better qualified to teach human anatomy in the context of a cadaver laboratory; (3) provide graduate physical therapy students additional opportunities to dissect and reinforce their anatomy knowledge; and (4) demonstrate the value of interdisciplinary collaboration. Eighty-one undergraduate students were exposed to the cadaver laboratory for four educational sessions throughout the semester. Course evaluations revealed that 93% of the undergraduate students reported that their experiences in the cadaver laboratory served to enhance their learning, and 97% reported that the use of human cadavers should be continued in future courses. This interdisciplinary collaboration allowed anatomy instruction to be expanded to previously unserved student groups as well as provided a mechanism for professional development of undergraduate anatomy faculty. This collaborative model may serve as a template to promote new program development to enhance faculty and student learning, while simultaneously encouraging interdisciplinary collaboration across the university.

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454.

Streamside management zones (SMZs) are regulated components of actively managed forests in the eastern United States, but width and overstory retention requirements vary widely among states. Within watershed-scale harvests, we evaluated the relative abundances of three riparian-associated songbirds (Acadian flycatcher, Empidonax virescens ; blue-headed vireo, Vireo solitarius ; and Louisiana waterthrush, Parkesia motacilla) near Appalachian headwater streams receiving three SMZ treatments representative of a range of current state SMZ regulations in the eastern United States. Prior to harvest, all species were relatively abundant near all stream types, including ephemerals. Abundances of all species were largely similar among SMZ treatments at 3 years and 10 years postharvest and were higher along streams with greater residual canopy cover within 100 m. Forested SMZs as prescribed by current regulations can sustain these species in managed forests up to 10 years postharvest, but abundance appears to be driven largely by canopy cover retention in adjacent stands. Managing habitat for riparian-associated songbirds in harvested watersheds will require expanding the extent of focus beyond the local stream corridor to include consideration of silvicultural systems that retain greater canopy cover in surrounding forests. Where conservation of riparian-associated songbirds is a management objective, SMZ protection should be extended to all streams. Study Implications: The results of this study suggest that forested streamside management zones (SMZs) mandated by contemporary best management practices (BMPs) in Kentucky and other eastern US states can sustain similar abundances of three riparian-associated songbirds in managed watersheds up to 10 years after harvest. Overstory retention in SMZs along perennial and intermittent streams is common across the eastern United States, but state BMPs rarely require retention along ephemeral streams. Target songbird species occupied forests along all stream types prior to harvest, and their postharvest abundances were positively associated with canopy retention within 100 m of streams. If long-term occupancy of managed stands by riparian-associated songbirds is an objective, we recommend using SMZs around all streams, including ephemerals, and implementing at least moderate-retention silvicultural systems in areas within 100 m of streams. [ABSTRACT FROM AUTHOR]