Accounting for early job turnover in recent pediatric surgery fellowship graduates: An American Pediatric Surgical Association Membership and Credentials Committee study

PURPOSE: Employment opportunities for graduating pediatric surgeons vary from year to year. Significant turnover among new employees indicates fellowship graduates may be unsophisticated in choosing job opportunities which will ultimately be satisfactory for themselves and their families. The purpose of this study was to assess what career, life, and social factors contributed to the turnover rates among pediatric surgeons in their first employment position. METHODS: American Pediatric Surgical Association members who completed fellowship training between 2011 and 2016 were surveyed voluntarily. Only those who completed training in a pediatric surgery fellowship sanctioned by the American Board of Surgery and whose first employment involved the direct surgical care of patients were included. The survey was completed electronically and the results were evaluated using chi-squared analysis to determine which independent variables contributed to a dependent outcome of changing place of employment. RESULTS: 110 surveys were returned with respondents meeting inclusion criteria. 13 (11.8%) of the respondents changed jobs within the study period and 97 (88.2%) did not change jobs. Factors identified that likely contributed to changing jobs included a perceived lack of opportunity for career [p = <0.001] advancement and the desire to no longer work at an academic or teaching facility [p = 0.013]. Others factors included excessive case load [p = 0.006]; personal conflict with partners or staff [p = 0.007]; career goals unfulfilled by practice [p = 0.011]; lack of mentorship in partners [p = 0.026]; and desire to be closer to the surgeon's or their spouse's family [p = 0.002]. CONCLUSIONS: Several factors appear to play a role in motivating young pediatric surgeons to change jobs early in their careers. These factors should be taken into account by senior pediatric fellows and their advisors when considering job opportunities

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40X magnification in 2.5 seconds per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts

In vivo reference point indentation measurement variability in skeletally mature inbred mice

Reference point indentation (RPI) was developed to measure material-level mechanical properties of bone in vivo. Studies using RPI in vivo have discriminated between human subjects with previous skeletal fractures and those without and among dogs given different anti-remodeling drugs. Recently, this technology was extended to rats, providing the first in vivo data for rodents. The goal of the present study was to perform in vivo RPI measurements in mice, the most common animal model used to study bone. Twelve 16-week-old female C57BL/6 mice were subjected to RPI (three tests) on the anterior tibia, followed by a repeat test session on the contralateral limb 28 days later. A custom MATLAB program was used to derive several outcome parameters from RPI force-displacement curves: first cycle indentation distance (ID-1st), ID increase (IDI), total ID (TID), first cycle unloading slope (US-1st) and first cycle energy dissipation (ED-1st). Data within an individual were averaged across the three tests for each time point. Within-animal variation of all RPI parameters on day 1 ranged from 12.8 to 33.4% and from 14.1 to 22.4% on day 28. Between-animal variation on day 1 ranged from 11.4% to 22.8% and from 7.5% to 24.7% on day 28. At both time points, within- and between-animals, US-1st was the least variable parameter and IDI was most variable. All parameters were nonsignificantly lower at day 28 compared with day 1. These data are important to demonstrate the feasibility of collecting bone material property data longitudinally in mice and will inform the design of future studies in terms of statistical power and appropriate sample size considerations

Predictors and clinical complications associated with antiphospholipid antibodies in sickle cell disease

Abstract Although a higher prevalence of antiphospholipid autoantibodies (aPL) has been observed in some cohorts of sickle cell disease (SCD) patients, the clinical risk factors for the development of aPL and its associated complications remain unclear. In a retrospective study of 63 SCD patients, a lower hemoglobin concentration and higher white blood cell count were independently associated with an elevated aPL. SCD patients with elevated aPL had increased pregnancy complications (≥3 miscarriages, preterm delivery, pre‐eclampsia) and venous thrombotic events. Our findings suggest that SCD may predispose to the generation of aPL and that aPL itself may contribute to the vasculopathy of SCD. Prospective testing for aPL is warranted in patients with SCD

Slideflow: Deep learning for digital histopathology with real-time whole-slide visualization

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi

Reduced skeletal muscle function is associated with decreased fiber cross-sectional area in the Cy/+ rat model of progressive kidney disease

BACKGROUND: The combination of skeletal muscle wasting and compromised function plays a role in the health decline commonly observed in chronic kidney disease (CKD) patients, but the pathophysiology of muscle mass/strength changes remains unclear. The purpose of this study was to characterize muscle properties in the Cy/+ rat model of spontaneously progressive CKD. METHODS: Leg muscle function and serum biochemistry of male Cy/+ (CKD) rats and their nonaffected littermates (NLs) were assessed in vivo at 25, 30 and 35 weeks of age. Architecture and histology of extensor digitorum longus (EDL) and soleus (SOL) muscles were assessed ex vivo at the conclusion of the experiment. We tested the hypothesis that animals with CKD have progressive loss of muscle function, and that this functional deficit is associated with loss of muscle mass and quality. RESULTS: Thirty-five-week-old CKD rats produced significantly lower maximum torque in ankle dorsiflexion and shorter time to maximum torque, and longer half relaxation time in dorsiflexion and plantarflexion compared with NL rats. Peak dorsiflexion torque (but not plantarflexion torque) in CKD remained steady from 25 to 35 weeks, while in NL rats, peak torque increased. Mass, physiologic cross-sectional area (CSA) and fiber-type (myosin heavy chain isoform) proportions of EDL and SOL were not different between CKD and NL. However, the EDL of CKD rats showed reduced CSAs in all fiber types, while only MyHC-1 fibers were decreased in area in the SOL. CONCLUSIONS: The results of this study demonstrate that muscle function progressively declines in the Cy/+ rat model of CKD. Because whole muscle mass and architecture do not vary between CKD and NL, but CKD muscles show reduction in individual fiber CSA, our data suggest that the functional decline is related to increased muscle fiber atrophy