Neuro-critical care skills training using a human patient simulator.

BACKGROUND: Neurosurgical trainees regularly encounter critical care situations. Traditionally, education was accomplished through lecture and experience. Increasingly, human patient simulators (HPS) are employed, allowing trainees to sharpen skills in a safe and realistic environment. We describe our experience using HPS in neurosurgical training. METHODS: We developed a critical care training program for residents and medical students using HPS. We used a hi-fidelity, lifelike Human Patient Simulator™ (HPS™) produced by Medical Education Technologies, Inc.™ to simulate realistic scenarios for trainee education. Topics included spinal shock, closed head injury, and cerebral vasospasm. A three-way evaluation model was employed to test validity, including pre- and post-exercise testing, survey feedback, and videotaped replay. The simulation exercises were conducted by a neuro-critical care attending, a senior neurosurgical resident, and a HPS technician. RESULTS: We currently have 29 participants. On a 20-point critical care multiple-choice exam for these participants, average improvement has been 4.5 points or 25%. In subgroup analysis, average improvement was 4.75 points (24%) amongst neurosurgery residents, 3.07 points (18%) amongst neurology residents, 7 points (38%) amongst general surgery residents, and 7 points (38%) amongst senior medical students. Post-exercise evaluations were overwhelmingly positive. CONCLUSIONS: Neurosurgical critical care education is important for safe and effective care for patients. Clinical experience and didactic lectures help trainees obtain a solid knowledge base, but do not provide the benefit for learning in a fail-safe environment. Through the use of HPS, we have enhanced the critical care education of our trainees.

Genomic expression discovery predicts pathways and opposing functions behind phenotypes.

Discovering states of genetic expression that are true to a high degree of certainty is likely to predict gene function behind biological phenotypes. The states of expression (up- or down-regulated) of 19200 cDNAs in 10 meningiomas are compared with normal brain by an algorithm that detects only 1 false measurement per 192000; 364 genes are discovered. The expression data accurately predict activation of signaling pathways and link gene function to specific phenotypes. Meningiomas appear to acquire aberrant phenotypes by disturbing the balanced expression of molecules that promote opposing functions. The findings expose interconnected genes and propose a role of genomic expression discovery in functional genomics of living systems.