Rarely taught in medical schools, clinical reasoning is the ability to discern the important from the unimportant and to arrive at accurate and efficient clinical conclusions. Identifying errors in reasoning is difficult; however, undetected clinical reasoning errors can have exponential consequences. As quality and patient safety come into focus, identifying and preventing clinical reasoning errors have become imperative. The authors present a case of a man sent for admission from a subspecialty clinic diagnosed with infliximab-induced serum sickness. Not countering the expert's diagnosis, initial workup failed to diagnose joint abscess and sepsis. Heuristics are mental shortcuts used to make decision making more efficient but can lead to error. The anchoring heuristic, premature closure, confirmation bias and the blind obedience heuristic are examples. Introspective surveillance and interactive hypothesis testing defend against heuristics. The authors conclude by discussing 4 types of hypersensitivity reactions, serum sickness in particular, and the chimeric nature of infliximab.
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http://dx.doi.org/10.1097/MAJ.0b013e3182083f14 | DOI Listing |
Int J Lang Commun Disord
January 2025
Department of Language and Cognition, University College London, London, UK.
Background: Global aphasia is a severe communication disorder affecting all language modalities, commonly caused by stroke. Evidence as to whether the functional communication of people with global aphasia (PwGA) can improve after speech and language therapy (SLT) is limited and conflicting. This is partly because cognition, which is relevant to participation in therapy and implicated in successful functional communication, can be severely impaired in global aphasia.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Laberit, Avda. de Catalunya, 9, València, 46020, Spain.
Background And Objective: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.
View Article and Find Full Text PDFDiagnosis (Berl)
January 2025
Scientific and Osteopathic Research Department, Institut de Formation en Ostéopathie du Grand Avignon IFO-GA, Avignon, France.
Objectives: Although cognitive biases are one of the most frequent causes of diagnostic errors, their influence remains underestimated in allied health professions, especially in osteopathy. Yet, a part of osteopathic clinical reasoning and diagnosis rely on the practitioner's intuition and subjective haptic perceptions. The aim of this study is to highlight links between the cognitive biases perceived by the practitioner to understand cognitive patterns during osteopathic diagnosis, and to suggest debiasing strategies.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Deakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
Background: Clinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties.
View Article and Find Full Text PDFNat Med
January 2025
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
The delivery of accurate diagnoses is crucial in healthcare and represents the gateway to appropriate and timely treatment. Although recent large language models (LLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning, their effectiveness in clinical diagnosis remains unproven. Here we present MedFound, a generalist medical language model with 176 billion parameters, pre-trained on a large-scale corpus derived from diverse medical text and real-world clinical records.
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