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Subacute encephalopathy with an electroencephalographic (EEG) pattern of generalized periodic triphasic sharp waves occurred in an 80-year-old woman with autopsy proven carcinomatous meningitis. Meningeal carcinomatosis should be included within the differential (clinical) diagnosis of such an EEG pattern.

Objective:The purpose of this study was to investigate the effect of the use of high-fidelity simulators with multidisciplinary teaching on self-reported confidence in residents.Methods:A total of 26 residents participated in a session led by a pediatric faculty member and a Neonatal Intensive Care Unit transport nurse using a high-fidelity pediatric simulator. Multiple scenarios were presented and each resident took turns in various roles. Pre-intervention surveys based on a 5-point Likert-type scale were given before the scenarios and were compared to the results of identical post-intervention surveys.Results:Statistically significant (p < 0.05) pre to post gains for self-confidence were observed. Improvements in confidence were analyzed using the mean difference. The largest improvement in confidence was seen in the ability to treat neonatal respiratory arrest. This was followed by the ability to supervise/run a code, and the ability to place an umbilical venous catheter.Conclusion:These results revealed that high-fidelity simulation-based training has significant positive gains in residents’ self-reported confidence.

Nutrition education is an essential component of medical education if new physicians are to be equipped to address common chronic diseases, including obesity and the associated diabetes, cardiovascular disease, and cancer. Most medical students recognize this need and desire nutrition education; however, finding time in a medical school curriculum and funding are challenging. Available, free online resources and small group exercises can be utilized to provide basic, up-to-date nutrition information to medical students.

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Purpose: We have observed that students’ performance in our PreClerkship curriculum does not align well with their USMLE STEP1 scores. Students at-risk of failing or underperforming on STEP1 have often excelled in our institutional assessments. We sought to test the validity and reliability of our course-assessments in predicting STEP1 scores, and in the process generate and validate a more accurate prediction model for STEP1 performance.Methods: Student pre-matriculation and course assessment data of the Class of 2020 (n = 76) is used to generate a stepwise STEP1 prediction model, which is tested with the students of the Class of 2021 (n = 71). Predictions are generated for the end of each course in the programing language R. For the Class of 2021, predicted STEP1 score is correlated with their actual STEP1 scores and data-agreement is tested with means-difference plots. A similar model is generated and tested for the Class of 2022.Results: STEP1 predictions based pre-matriculation data are unreliable and fail to identify at-risk students (R2 = 0.02). STEP1 predictions for most year 1 courses (anatomy, biochemistry, physiology) correlate poorly with students’ actual STEP1 scores (R2 = 0.30). STEP1 predictions improve for year 2 courses (microbiology, pathology and pharmacology), but reliable predictions are based on truly integrated courses with customized NBMEs as comprehensive exams (0.66). Predictions based on these NBMEs and integrated courses are reproducible for the Class of 2022.Conclusion: MCAT and undergraduate GPA are poor predictors of students’ STEP1 scores. Partially integrated courses with biweekly assessments do not promote problem-solving skills and leave students’ at-risk of failing STEP1. Only truly integrated courses with comprehensive assessments are reliable indicators of students STEP1 preparation.

Patient: Male, newbornFinal Diagnosis: Hypoxic ischemic encephalopathySymptoms: Arthrogryposis • bitemporal wasting • graf type IIa dysplasia • NAS symptomsMedication: —Clinical Procedure: —Specialty: Pediatrics and NeonatologyObjective:Congenital defects/diseasesBackground:With the increasing prevalence of substance use in pregnancy, the rates of neonatal abstinence syndrome (NAS) are dramatically increasing. There is little information on the use of multiple substances in adults, even less so of polysubstance abuse during pregnancy and the consequences for the fetus as well as the mother.Case Report:A newborn male born at 35 weeks presented post-delivery with hips bilaterally dislocated and hyperflexed. The patient’s legs fully extended and their shoulders were bilaterally mid-flexed with arms fully extended. This neonate was also reported to have bilateral hearing and vision loss as well as NAS symptoms of high-pitched crying and respiratory distress. During pregnancy the mother in this case study admitted to using buprenorphine, benzodiazepines, gabapentin, and heroin. The consequences of using this combination has not been well studied in pregnancy.Conclusions:The presented case had severe complications, likely due to maternal polysubstance use and poor prenatal care in pregnancy. Clonidine was used to control the NAS symptoms, ranitidine was used to treat the gastroesophageal reflux, and glycopyrronium bromide was used for the neonate’s excessive secretions. After delivery, the patient was placed on a nasal noninvasive cannula for respiratory distress and was transferred to a different hospital for treatment of the more serious comorbid conditions.

PurposeWe have observed that students' performance in our pre-clerkship curriculum does not align well with their United States Medical Licensing Examination (USMLE) STEP1 scores. Students at-risk of failing or underperforming on STEP1 have often excelled on our institutional assessments. We sought to test the validity and reliability of our course assessments in predicting STEP1 scores, and in the process, generate and validate a more accurate prediction model for STEP1 performance.MethodsStudent pre-matriculation and course assessment data of the Class of 2020 (n = 76) is used to generate a stepwise STEP1 prediction model, which is tested with the students of the Class of 2021 (n = 71). Predictions are developed at the time of matriculation and subsequently at the end of each course in the programing language R. For the Class of 2021, the predicted STEP1 score is correlated with their actual STEP1 scores, and data agreement is tested with means-difference plots. A similar model is generated and tested for the Class of 2022.ResultsSTEP1 predictions based on pre-matriculation data are unreliable and fail to identify at-risk students (R2 = 0.02). STEP1 predictions for most year one courses (anatomy, biochemistry, physiology) correlate poorly with students' actual STEP1 scores (R2 = 0.30). STEP1 predictions improve for year two courses (microbiology, pathology, and pharmacology). But integrated courses with customized NBMEs provide more reliable predictions (R2 = 0.66). Predictions based on these integrated courses are reproducible for the Class of 2022.ConclusionMCAT and undergraduate GPA are poor predictors of student's STEP1 scores. Partially integrated courses with biweekly assessments do not promote problem-solving skills and leave students' at-risk of failing STEP1. Only courses with integrated and comprehensive assessments are reliable indicators of students' STEP1 preparation.