Objectives: Graduate medical education is shifting toward an outcome-based paradigm, where physicians are evaluated for competency using well-defined criteria. Our aim was to learning objectives and a testing tool to assess competency in the management of mechanical ventilation for infants, children, and adolescents and to verify that the test was reliable and valid.
Design: Prospective reliability and validity study.
Setting: Large, university-affiliated academic hospital.
Subjects: Sixty-one total subjects from five different academic centers divided into three groups of varying experience. The groups were second- and third-year pediatric residents (Novice), second- and third-year pediatric critical care fellows (Advanced), and pediatric critical care faculty (Expert).
Interventions: None.
Measurements And Main Results: Ten learning objectives considered important for the management of pediatric mechanical ventilation were developed from expert opinion and current evidence. Based on these objectives, a 35-question multiple choice, knowledge- and case-based test was created. Content validity was achieved by consensus of three experts in pediatric critical care medicine evaluating whether the questions reflected the learning objectives and the responses were consistent with current practice and evidence-based medicine. The test was then administered to the three groups to establish construct validity. The "Novice" group scored a mean of 34.6% (95% CI, 28-41%), the "Advanced" group a mean of 59.4% (95% CI, 53-65%), and the "Expert" group a mean of 74.8% (95% CI, 69-80%), with p less than 0.01 for all comparisons. As determined by Hoyt's analysis, the reliability coefficient was 0.89, reflecting excellent reliability.
Conclusions: This is the first description of specific learning objectives for management of pediatric mechanical ventilation and the first validated and reliable testing tool for assessing knowledge. This tool could be used by fellowship programs to assess fellow competency and identify knowledge gaps in this area prior to completion of training. Further work must be done to determine the criteria for determination of competency.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/PCC.0000000000000195 | DOI Listing |
J Med Internet Res
January 2025
Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Background: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
Objective: This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.
Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University.
Oper Neurosurg (Hagerstown)
July 2024
Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal , Quebec , Canada.
Background And Objectives: Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment.
View Article and Find Full Text PDFJAMA Cardiol
January 2025
Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois.
Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.
Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).
J Speech Lang Hear Res
January 2025
Centre for Language Studies, Radboud University, Nijmegen, the Netherlands.
Purpose: In this review article, we present an extensive overview of recent developments in the area of dysarthric speech research. One of the key objectives of speech technology research is to improve the quality of life of its users, as evidenced by the focus of current research trends on creating inclusive conversational interfaces that cater to pathological speech, out of which dysarthric speech is an important example. Applications of speech technology research for dysarthric speech demand a clear understanding of the acoustics of dysarthric speech as well as of speech technologies, including machine learning and deep neural networks for speech processing.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!