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

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice

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

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