A Delphi Consensus to Identify Perioperative Antibiotic Prescribing Best Practices in Mohs Surgery

Abstract: Surgical site infections (SSI) make a significant global contribution to morbidity, mortality, and cost while remaining one of the most preventable causes of healthcare-associated infection. Perioperative antibiotics are a mainstay of prevention, but antibiotics are also associated with cost, risk, and increasing resistance. Dermatology is responsible for more oral antibiotic prescriptions than any other discipline. Despite a trend toward conservative prescribing practices and antibiotic stewardship in dermatology overall, antibiotic prescriptions in dermatologic surgery continue to increase, with a notable rise in short-term perioperative prescribing. There is currently a lack of evidence-based perioperative antibiotic prescribing guidelines within the dermatology literature. Evidence supports the need for specific, up-to-date recommendations regarding antibiotic management in the setting of dermatologic surgery. This QI project aims to review and synthesize current recommendations in the literature and identify best practices for developing standardized, appropriate use criteria for perioperative use of antibiotics in dermatologic surgery

MR. FISCAL: The Effects of a Financial Education Curriculum on Family Medicine Residents\u27 and Fellows\u27 Financial Well-Being and Literacy

CONTEXT: Financial education is not routinely offered during medical training. Residents and fellows thus have low financial literacy, high debt, and deficits in their financial preparedness. Poor financial literacy contributes to the ever-growing problems of physician stress, job dissatisfaction, burnout, and depression within primary care. It is postulated that implementation of a financial education curriculum for family medicine physicians-in-training will improve their sense of financial well-being and literacy. OBJECTIVE: This study aims to determine the effects of a formal financial education curriculum on family medicine residents\u27 and fellows\u27 financial well-being and literacy. DESIGN: Solomon four group. PARTICIPANTS: Convenience sample, voluntary participation. Residents and fellows at 16 family medicine residency programs (military, academic/university, and community-based) in the U.S. INTERVENTION: A standardized video-based financial education curriculum entitled Medical Residency Financial Skills Curriculum to Advance Literacy (MR. FISCAL). Topics include: money management, credit, debt management, risk management, investment and retirement planning. Educational content designed by the research team using the Institute for Financial Literacy National Standards for Adult Financial Literacy Education content. INSTRUMENT: Anonymous, web-based, 24-question survey, administered via Qualtrics. Survey is comprised of InCharge Financial Distress/Financial Well-Being (IFDFW) Scale measuring perceived levels of financial distress/well-being, plus 16 additional questions collecting demographic and self-reported financial data. MAIN OUTCOME MEASURES: The effect of this financial education curriculum on family medicine residents’ and fellows’ financial well-being and literacy as measured by the validated and reliable IFDFW scale and comparison of pre and post-intervention self-reported financial data. RESULTS: Work-in-progress. Anticipate comparison of pretest-posttest intervention versus posttest-only control group data. Additional statistical analysis will compare level of training, type of residency program, other demographics, financial data. CONCLUSION: There is currently a paucity of information on financial well-being and literacy among family medicine residents and fellows. This financial curriculum could be shared throughout primary care if improvements are observed

Initial Studies of Cavity Fault Prediction at Jefferson Laboratory

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming dat

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed

Using AI for Management of Field Emission in SRF Linacs

Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high levels of field emission based on control system data such as radiation levels, cryogenic readbacks, and vacuum loads. The gradients on the most offending cavities will be reduced and compensated for by increasing the gradients on least offensive cavities. Training data will be collected during this year’s operational program and initial implementation of AI models will be deployed. Preliminary results and future plans are presented

Psychosocial risk factors for obesity among women in a family planning clinic

BACKGROUND: The epidemiology of obesity in primary care populations has not been thoroughly explored. This study contributes to filling this gap by investigating the relationship between obesity and different sources of personal stress, mental health, exercise, and demographic characteristics. METHODS: A cross-sectional survey using a convenience sample. Five hundred women who attended family planning clinics were surveyed and 274 provided completed answers to all of the questions analyzed in this study. Exercise, self-rated mental health, stress, social support, and demographic variables were included in the survey. Multiple logistic regression analysis was performed. RESULTS: After adjusting for mental health, exercise, and demographic characteristics of subjects, analysis of the data indicated that that being having a large family and receiving no support from parents were related to obesity in this relatively young low-income primary care sample, but self-reported stress and most types of social support were not significant. CONCLUSION: Obesity control programs in primary care centers directed at low-income women should target women who have large families and who are not receiving support from their parents

Calibration of myocardial T2 and T1 against iron concentration.

BACKGROUND: The assessment of myocardial iron using T2* cardiovascular magnetic resonance (CMR) has been validated and calibrated, and is in clinical use. However, there is very limited data assessing the relaxation parameters T1 and T2 for measurement of human myocardial iron. METHODS: Twelve hearts were examined from transfusion-dependent patients: 11 with end-stage heart failure, either following death (n=7) or cardiac transplantation (n=4), and 1 heart from a patient who died from a stroke with no cardiac iron loading. Ex-vivo R1 and R2 measurements (R1=1/T1 and R2=1/T2) at 1.5 Tesla were compared with myocardial iron concentration measured using inductively coupled plasma atomic emission spectroscopy. RESULTS: From a single myocardial slice in formalin which was repeatedly examined, a modest decrease in T2 was observed with time, from mean (± SD) 23.7 ± 0.93 ms at baseline (13 days after death and formalin fixation) to 18.5 ± 1.41 ms at day 566 (p<0.001). Raw T2 values were therefore adjusted to correct for this fall over time. Myocardial R2 was correlated with iron concentration [Fe] (R2 0.566, p<0.001), but the correlation was stronger between LnR2 and Ln[Fe] (R2 0.790, p<0.001). The relation was [Fe] = 5081•(T2)-2.22 between T2 (ms) and myocardial iron (mg/g dry weight). Analysis of T1 proved challenging with a dichotomous distribution of T1, with very short T1 (mean 72.3 ± 25.8 ms) that was independent of iron concentration in all hearts stored in formalin for greater than 12 months. In the remaining hearts stored for <10 weeks prior to scanning, LnR1 and iron concentration were correlated but with marked scatter (R2 0.517, p<0.001). A linear relationship was present between T1 and T2 in the hearts stored for a short period (R2 0.657, p<0.001). CONCLUSION: Myocardial T2 correlates well with myocardial iron concentration, which raises the possibility that T2 may provide additive information to T2* for patients with myocardial siderosis. However, ex-vivo T1 measurements are less reliable due to the severe chemical effects of formalin on T1 shortening, and therefore T1 calibration may only be practical from in-vivo human studies

The politics of regulatory enforcement and compliance: Theorizing and operationalizing political influences

There is broad consensus in the literature on regulatory enforcement and compliance that politics matters. However, there is little scholarly convergence on what politics is or rigorous theorization and empirical testing of how politics matters. Many enforcement and compliance studies omit political variables altogether. Among those that address political influences on regulatory outcomes, politics has been defined in myriad ways and, too often, left undefined. Even when political constructs are explicitly operationalized, the mechanisms by which they influence regulatory outcomes are thinly hypothesized or simply ignored. If politics is truly as important to enforcement and compliance outcomes as everyone in the field seems to agree, regulatory scholarship must make a more sustained and systematic effort to understand their relationship, because overlooking this connection risks missing what is actually driving regulatory outcomes. This article examines how the construct of “politics” has been conceptualized in regulatory theory and analyzes how it has been operationalized in empirical studies of regulatory enforcement and compliance outcomes. It brings together scholarship across disciplines that rarely speak but have much to say to one another on this subject in order to constitute a field around the politics of regulation. The goal is to sharpen theoretical and empirical understandings of when and how regulation works by better accounting for the role politics plays in its enforcement