Background: Despite increased use of rigid bronchoscopy (RB) for therapeutic indications and recommendations from professional societies to use performance-based competency, an assessment tool has not been utilized to measure the competency of trainees to perform RB in clinical settings.
Objectives: The aim of the study was to evaluate a previously developed assessment tool - Rigid Bronchoscopy Tool for Assessment of Skills and Competence (RIGID-TASC) - for determining the RB learning curve of interventional pulmonary (IP) trainees in the clinical setting and explore the variability of learning curve of trainees.
Methods: IP fellows at 4 institutions were enrolled. After preclinical simulation training, all RBs performed in patients were scored by faculty using RIGID-TASC until competency threshold was achieved. Competency threshold was defined as unassisted RB intubation and navigation through the central airways on 3 consecutive patients at the first attempt with a minimum score of 89. A regression-based model was devised to construct and compare the learning curves.
Results: Twelve IP fellows performed 178 RBs. Trainees reached the competency threshold between 5 and 24 RBs, with a median of 15 RBs (95% CI, 6-21). There were differences among trainees in learning curve parameters including starting point, slope, and inflection point, as demonstrated by the curve-fitting model. Subtasks that required the highest number of procedures (median = 10) to gain competency included ability to intubate at the first attempt and intubation time of <60 s.
Conclusions: Trainees acquire RB skills at a variable pace, and RIGID-TASC can be used to assess learning curve of IP trainees in clinical settings.
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http://dx.doi.org/10.1159/000514627 | DOI Listing |
J Phys Ther Educ
January 2025
Introduction: This study examines the ability of human readers, recurrence quantification analysis (RQA), and an online artificial intelligence (AI) detection tool (GPTZero) to distinguish between AI-generated and human-written personal statements in physical therapist education program applications.
Review Of Literature: The emergence of large language models such as ChatGPT and Google Gemini has raised concerns about the authenticity of personal statements. Previous studies have reported varying degrees of success in detecting AI-generated text.
J Neuroophthalmol
December 2024
Division of Ophthalmology (EB-S, AS, AA-A, AS-B, DW, SS, FC), Department of Surgery, University of Calgary, Calgary, Canada; Department of Biomedical Engineering (CN), University of Calgary, Calgary, Canada; Departments of Neurology (LBDL) and Ophthalmology (LBDL), University of Michigan, Ann Arbor, Michigan; and Department of Clinical Neurosciences (SS, FC), University of Calgary, Calgary, Canada.
Background: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Kobayashi Hospital, 510 Imaichi, Izumo City, Shimane, 693-0001, Japan.
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort.
View Article and Find Full Text PDFMenopause
January 2025
From the School of Nursing and Rehabilitation, Shandong University, Jinan, Shandong, China.
Objective: This study aims to develop and validate a machine learning model for identifying individuals within the nursing population experiencing severe subjective cognitive decline (SCD) during the menopause transition, along with their associated factors.
Methods: A secondary analysis was performed using cross-sectional data from 1,264 nurses undergoing the menopause transition. The data set was randomly split into training (75%) and validation sets (25%), with the Bortua algorithm employed for feature selection.
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
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