The Impact of United States Medical Licensing Examination Step 1 Transitioning To Pass/Fail on Medical Student Perception of Research Needed to Match Into One's Preferred Specialty.

Purpose To evaluate how the transition of United States Medical Licensing Examination (USMLE) Step 1 to a pass/fail scoring influenced medical student perceptions of the importance of research required to match into their preferred residency specialty. Methods A 14-item survey was distributed by e-mail to medical students at one medical school in the southeastern United States in November of 2021. Responses were compared between medical students taking USMLE Step 1 pass/fail in the future and medical students taking USMLE Step 1 for a three-digit score. Results A total of 168 medical students responded to the survey with 98 respondents who planned on taking USMLE Step 1 pass/fail (45 first-year medical students (MS1) and 53 MS2) and 70 respondents who took USMLE Step 1 for a numerical score (37 MS3 and 33 MS4). There were no differences in how each cohort scored the level of importance of research in matching into their preferred residency specialty (p=0.10); however, those taking USMLE Step 1 pass/fail believe an average of 4.6 research experiences are necessary to match into their preferred residency, compared to only 3.4 research experiences for those who took it for a numerical score (p=0.04). Conclusion No statistically significant difference in the perceived importance of research in matching into one's preferred residency specialty was found between cohorts. However, the pass/fail cohort believes they will need more research experiences to match their chosen specialty than the numerical score cohort. Results could indicate that students participate in more research and extracurricular activities to be more competitive for residency applications.

Analytical validation of a novel comprehensive genomic profiling informed circulating tumor DNA monitoring assay for solid tumors.

Emerging technologies focused on the detection and quantification of circulating tumor DNA (ctDNA) in blood show extensive potential for managing patient treatment decisions, informing risk of recurrence, and predicting response to therapy. Currently available tissue-informed approaches are often limited by the need for additional sequencing of normal tissue or peripheral mononuclear cells to identify non-tumor-derived alterations while tissue-naïve approaches are often limited in sensitivity. Here we present the analytical validation for a novel ctDNA monitoring assay, FoundationOne®Tracker. The assay utilizes somatic alterations from comprehensive genomic profiling (CGP) of tumor tissue. A novel algorithm identifies monitorable alterations with a high probability of being somatic and computationally filters non-tumor-derived alterations such as germline or clonal hematopoiesis variants without the need for sequencing of additional samples. Monitorable alterations identified from tissue CGP are then quantified in blood using a multiplex polymerase chain reaction assay based on the validated SignateraTM assay. The analytical specificity of the plasma workflow is shown to be 99.6% at the sample level. Analytical sensitivity is shown to be >97.3% at ≥5 mean tumor molecules per mL of plasma (MTM/mL) when tested with the most conservative configuration using only two monitorable alterations. The assay also demonstrates high analytical accuracy when compared to liquid biopsy-based CGP as well as high qualitative (measured 100% PPA) and quantitative precision (<11.2% coefficient of variation).

An Application of Spatio-temporal Modeling to Finite Population Abundance Prediction.

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be a lack of resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey.