Diagnosis最新文献

Objectives: We sought within an ambulatory safety study to understand if the Revised Safer Dx instrument may be helpful in identification of diagnostic missed opportunities in care of children with type 1 diabetes (T1D) and autism spectrum disorder (ASD).

Methods: We reviewed two months of emergency department (ED) encounters for all patients at our tertiary care site with T1D and a sample of such encounters for patients with ASD over a 15-month period, and their pre-visit communication methods to better understand opportunities to improve diagnosis. We applied the Revised Safer Dx instrument to each diagnostic journey. We chose potentially preventable ED visits for hyperglycemia, diabetic ketoacidosis, and behavioral crises, and reviewed electronic health record data over the prior three months related to the illness that resulted in the ED visit.

Results: We identified 63 T1D and 27 ASD ED visits. Using the Revised Safer Dx instrument, we did not identify any potentially missed opportunities to improve diagnosis in T1D. We found two potential missed opportunities (Safer Dx overall score of 5) in ASD, related to potential for ambulatory medical management to be improved. Over this period, 40 % of T1D and 52 % of ASD patients used communication prior to the ED visit.

Conclusions: Using the Revised Safer Dx instrument, we uncommonly identified missed opportunities to improve diagnosis in patients who presented to the ED with potentially preventable complications of their chronic diseases. Future researchers should consider prospectively collected data as well as development or adaptation of tools like the Safer Dx.

Objectives: To analyze the Big Three diagnostic errors (malignant neoplasms, cardiovascular diseases, and infectious diseases) through internists' self-reflection on their most memorable diagnostic errors.

Methods: This secondary analysis study, based on a web-based cross-sectional survey, recruited participants from January 21 to 31, 2019. The participants were asked to recall the most memorable diagnostic error cases in which they were primarily involved. We gathered data on internists' demographics, time to error recognition, and error location. Factors causing diagnostic errors included environmental conditions, information processing, and cognitive bias. Participants scored the significance of each contributing factor on a Likert scale (0, unimportant; 10, extremely important).

Results: The Big Three comprised 54.1 % (n=372) of the 687 cases reviewed. The median physician age was 51.5 years (interquartile range, 42-58 years); 65.6 % of physicians worked in hospital settings. Delayed diagnoses were the most common among malignancies (n=64, 46 %). Diagnostic errors related to malignancy were frequent in general outpatient settings on weekdays and in the mornings and were not identified for several months following the event. Environmental factors often contributed to cardiovascular disease-related errors, which were typically identified within days in emergency departments, during night shifts, and on holidays. Information gathering and interpretation significantly impacted infectious disease diagnoses.

Conclusions: The Big Three accounted for the majority of cases recalled by Japanese internists. The most relevant contributing factors were different for each of the three categories. Addressing these errors may require a unique approach based on the disease associations.

Objectives: Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions.

Methods: This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features.

Results: Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases.

Conclusions: This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes.

Objectives: The potential of artificial intelligence (AI) chatbots, particularly the fourth-generation chat generative pretrained transformer (ChatGPT-4), in assisting with medical diagnosis is an emerging research area. While there has been significant emphasis on creating lists of differential diagnoses, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in these lists. This short communication aimed to assess the accuracy of ChatGPT-4 in evaluating lists of differential diagnosis compared to medical professionals' assessments.

Methods: We used ChatGPT-4 to evaluate whether the final diagnosis was included in the top 10 differential diagnosis lists created by physicians, ChatGPT-3, and ChatGPT-4, using clinical vignettes. Eighty-two clinical vignettes were used, comprising 52 complex case reports published by the authors from the department and 30 mock cases of common diseases created by physicians from the same department. We compared the agreement between ChatGPT-4 and the physicians on whether the final diagnosis was included in the top 10 differential diagnosis lists using the kappa coefficient.

Results: Three sets of differential diagnoses were evaluated for each of the 82 cases, resulting in a total of 246 lists. The agreement rate between ChatGPT-4 and physicians was 236 out of 246 (95.9 %), with a kappa coefficient of 0.86, indicating very good agreement.

Conclusions: ChatGPT-4 demonstrated very good agreement with physicians in evaluating whether the final diagnosis should be included in the differential diagnosis lists.

Introduction: Clinical reasoning is crucial in medical practice, yet its teaching faces challenges due to varied clinical experiences, limited time, and absence from competency frameworks. Despite efforts, effective teaching methodologies remain elusive. Strategies like the One Minute Preceptor (OMP) and SNAPPS are proposed as solutions, particularly in workplace settings. SNAPPS, introduced in 2003, offers a structured approach but lacks comprehensive evidence of its effectiveness. Methodological shortcomings hinder discerning its specific effects. Therefore, a systematic review is proposed to evaluate SNAPPS' impact on clinical reasoning teaching.

Content: We searched PubMed, EMBASE, and CINAHL for randomized controlled trials (RCTs) comparing SNAPPS against other methods. Data selection and extraction were performed in duplicate. Bias and certainty of evidence were evaluated using Cochrane RoB-2 and GRADE approach.

Summary: We identified five RCTs performed on medical students and residents. Two compared SNAPPS with an active control such as One Minute Preceptor or training with feedback. None reported the effects of SNAPPS in workplace settings (Kirkpatrick Level 3) or patients (Kirkpatrick Level 4). Low to moderate certainty of evidence suggests that SNAPPS increases the total presentation length by increasing discussion length. Low to moderate certainty of evidence may increase the number of differential diagnoses and the expression of uncertainties. Low certainty of evidence suggests that SNAPPS may increase the odds of trainees initiating a management plan and seeking clarification.

Outlook: Evidence from this systematic review suggests that SNAPPS has some advantages in terms of clinical reasoning, self-directed learning outcomes, and cost-effectiveness. Furthermore, it appears more beneficial when used by residents than medical students. However, future research should explore outcomes outside SNAPPS-related outcomes, such as workplace or patient-related outcomes.

Objectives: Type 2 diabetes (T2DM) is associated with increased risk for cardiovascular disease (CVD). Whether screen-detected T2DM, based on fasting plasma glucose (FPG) or on HbA_1c, are associated with different risks of incident CVD in high-risk populations and which one is preferable for diabetes screening in these populations, remains unclear.

Methods: A total of 8,274 high-risk CVD participants were included from the UCC-SMART cohort. Participants were divided into groups based on prior T2DM diagnosis, and combinations of elevated/non-elevated FPG and HbA_1c (cut-offs at 7 mmol/L and 48 mmol/mol, respectively): Group 0: known T2DM; group 1: elevated FPG/HbA_1c; group 2: elevated FPG, non-elevated HbA_1c; group 3: non-elevated FPG, elevated HbA_1c; group 1 + 2: elevated FPG, regardless of HbA_1c; group 1 + 3: elevated HbA_1c, regardless of FPG; and group 4 (reference), non-elevated FPG/HbA_1c.

Results: During a median follow-up of 6.3 years (IQR 3.3-9.8), 712 cardiovascular events occurred. Compared to the reference (group 4), group 0 was at increased risk (HR 1.40; 95 % CI 1.16-1.68), but group 1 (HR 1.16; 95 % CI 0.62-2.18), 2 (HR 1.18; 95 % CI 0.84-1.67), 3 (HR 0.61; 95 % CI 0.15-2.44), 1 + 2 (HR 1.17; 95 % CI 0.86-1.59) and 1 + 3 (HR 1.01; 95 % CI 0.57-1.79) were not. However, spline interpolation showed a linearly increasing risk with increasing HbA_1c/FPG, but did not allow for identification of other cut-off points.

Conclusions: Based on current cut-offs, FPG and HbA_1c at screening were equally related to incident CVD in high-risk populations without known T2DM. Hence, neither FPG, nor HbA_1c, is preferential for diabetes screening in this population with respect to risk of incident CVD.

Objectives: Intraneural ganglionic cysts are non-neoplastic cysts that can cause signs and symptoms of peripheral neuropathy. However, the scarcity of such cases can lead to cognitive biases. Early surgical exploration of space occupying lesions plays an important role in identification and improving the outcomes for intraneural ganglionic cysts.

Case presentation: This patient presented with loss of sensation on the right sole with tingling numbness for six months. A diagnosis of tarsal tunnel syndrome was made. Nerve conduction study revealed that the mixed nerve action potential (NAP) was absent in the right medial and lateral plantar nerves. The magnetic resonance imaging (MRI) found a cystic lesion measuring 1.4×1.8×3.8 cm as the presumed cause of the neuropathy. Surgical exploration revealed a ganglionic cyst traversing towards the flexor retinaculum with baby cysts. The latter finding came as a surprise to the treating surgeon and was confirmed to be an intraneural ganglionic cyst based on the histopathology report.

Conclusions: Through integrated commentary by a case discussant and reflection by an orthopedician, this case highlights the significance of the availability heuristic, confirmation bias, and anchoring bias in a case of rare disease. Despite diagnostic delays, a medically knowledgeable patient's involvement in their own care lead to a more positive outcome. A fish-bone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic delay. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl related to availability heuristic and the sunk cost fallacy.

Insight has been studied as an element of problem solving in the field of cognitive psychology and may play an important role in clinical reasoning. We propose a new strategy based on theories that promote insight that may help generate further diagnostic hypotheses by reviewing the interpretation of a case and an individual's list of differential diagnoses from multiple perspectives: formation (F), re-encoding (R), analogy (A), modification (M), elaboration (E), and deliberation (D) (FRAMED). The FRAMED strategy may help clinicians overcome misinterpretations and cognitive bias by systematically reflecting on previous clinical reasoning processes from multiple perspectives.