Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare.

Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we propose a crowdsourcing (CS) framework enriched with quality control measures at the pre-, real-time-, and post-data gathering stages.

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

Objective: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence.

Intentional interprofessional leadership in maternal and child health.

Purpose: This paper aims to describe an interprofessional leadership training program curriculum implemented by a new maternal and child health leadership training program, its collaboration with a well-established leadership consortium, the measures taken to evaluate this training and implications for other leadership programs.

Design/methodology/approach: The intentional leadership program weaves together the complementary core threads to create strong sets of skills in the areas of personal leadership, leading and influencing others and creating effective interprofessional partnerships with others around women and children's health.

Findings: The strong emphasis on the incorporation of leadership competencies coupled with evidence-based leadership training strengthens students' clinical skills, enhances workforce development and increases interdisciplinary health care practices.

Incidence of delirium in children with cancer.

There are limited data on the incidence of delirium in children with cancer. We performed a retrospective chart review of all pediatric oncology admissions over a 1 year period to determine the incidence of delirium in this population. We identified seven patients with delirium (10% incidence).

Rehabilitation of children with traumatic brain injury: descriptive analysis of a nationwide sample using the WeeFIM.

Objective: To describe functional capability at admission and discharge of children with traumatic brain injury (TBI) in rehabilitation settings.

Design: Descriptive analysis.

Setting: Inpatient pediatric rehabilitation hospitals in the United States.

Effect of shortened length of stay on functional and educational outcome after pediatric rehabilitation.

Objective: The purpose of this study was to assess changes in the length of stay and its effect on effectiveness and return to school in an inpatient pediatric rehabilitation unit during a 5-yr period from fiscal year 1997 through 2001.

Design: We reviewed prospectively collected data for a cohort of 321 children during fiscal years 1997-2001.

Results: Length of stay was significantly shortened, with mean lengths of stay of 58.

Brain imaging as a predictor of early functional outcome following traumatic brain injury in children, adolescents, and young adults.

Objectives: A depth of lesion (DOL) model using brain imaging has been proposed to aid in medical decision-making and planning for rehabilitation resource needs. The purpose of this study was to determine the early prognostic value of a DOL classification system for children and young adults following severe traumatic brain injury.

Methods And Outcome Measures: CT/MRI brain imaging studies on 92 patients, aged 3 to 21, admitted to the Kluge Children's Rehabilitation Center, University of Virginia, were evaluated to determine DOL.