Diagnosis最新文献

Objectives: Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers.

Methods: The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields.

Results: The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper.

Conclusions: This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal.

Objectives: To describe patterns observed in antibody titer trendlines in patients with mast cell activation syndrome (MCAS, a prevalent but underrecognized chronic multisystem inflammatory disorder of great clinical heterogeneity) and offer clinical lessons learned from such pattern recognition.

Methods: The available records of 104 MCAS patients drawn from the authors' practices were reviewed, including all antibody tests therein.

Results: All patients had positive/elevated antibodies of various sorts at various points, but for most of the antibodies which were found to be positive at least some points, the diseases classically associated with those antibodies were not present, marking such antibodies as clinically insignificant mimickers (likely consequent to inflammatory effects of MCAS on the immune system itself driving spurious/random antibody production) rather than "on-target" and pathogenic antibodies reflecting true disease warranting treatment. We also observed two distinct patterns in trendlines of the titers of the mimickers vs. the trendline pattern expected in a true case of an antibody-associated disease (AAD).

Conclusions: Our observations suggest most positive antibody tests in MCAS patients represent detection of clinically insignificant mimicking antibodies. As such, to reduce incorrect diagnoses of AADs and inappropriate treatment in MCAS patients, caution is warranted in interpreting positive antibody tests in these patients. Except in clinically urgent/emergent situations, patience in determining the trendline of a positive antibody in an MCAS patient, and more carefully assessing whether the AAD is truly present, is to be preferred.

Objectives: Achieving diagnostic excellence on medical wards requires teamwork and effective team dynamics. However, the study of ward team dynamics in teaching hospitals is relatively underdeveloped. We aim to enhance understanding of how ward team members interact in the diagnostic process and of the underlying behavioral, psychological, and cognitive mechanisms driving team interactions.

Methods: We used mixed-methods to develop and refine a conceptual model of how ward team dynamics in an academic medical center influence the diagnostic process. First, we systematically searched existing literature for conceptual models and empirical studies of team dynamics. Then, we conducted field observations with thematic analysis to refine our model.

Results: We present a conceptual model of how medical ward team dynamics influence the diagnostic process, which serves as a roadmap for future research and interventions in this area. We identified three underexplored areas of team dynamics that are relevant to diagnostic excellence and that merit future investigation (1): ward team structures (e.g., team roles, responsibilities) (2); contextual factors (e.g., time constraints, location of team members, culture, diversity); and (3) emergent states (shared mental models, psychological safety, team trust, and team emotions).

Conclusions: Optimizing the diagnostic process to achieve diagnostic excellence is likely to depend on addressing all of the potential barriers and facilitators to ward team dynamics presented in our model.

Objectives: To assess the usefulness of case reports as sources for research on diagnostic errors in uncommon diseases and atypical presentations.

Content: We reviewed 563 case reports of diagnostic error. The commonality of the final diagnoses was classified based on the description in the articles, Orphanet, or epidemiological data on available references; the typicality of presentation was classified based on the description in the articles and the judgment of the physician researchers. Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC), and Generic Diagnostic Pitfalls (GDP) taxonomies were used to assess the factors contributing to diagnostic errors.

Summary and outlook: Excluding three cases in that commonality could not be classified, 560 cases were classified into four categories: typical presentations of common diseases (60, 10.7 %), atypical presentations of common diseases (35, 6.2 %), typical presentations of uncommon diseases (276, 49.3 %), and atypical presentations of uncommon diseases (189, 33.8 %). The most important DEER taxonomy was "Failure/delay in considering the diagnosis" among the four categories, whereas the most important RDC and GDP taxonomies varied with the categories. Case reports can be a useful data source for research on the diagnostic errors of uncommon diseases with or without atypical presentations.

Objectives: Neurofilament light (NfL) chain is a marker of neuroaxonal damage in various neurological diseases. Here we quantitated NfL levels in the cerebrospinal fluid (CSF) and serum from patients with multiple sclerosis (MS) and controls, using the R-PLEX NfL assay, which employs advanced Meso Scale Discovery^® (MSD) electrochemiluminescence (ECL)-based detection technology.

Methods: NfL was quantitated in samples from 116 individuals from two sites (Ottawa Hospital Research Institute and Mayo Clinic), consisting of patients with MS (n=71) and age- and sex-matched inflammatory neurological controls (n=13) and non-inflammatory controls (n=32). Correlation of NfL levels between CSF and serum was assessed in paired samples in a subset of MS patients and controls (n=61). Additionally, we assessed the correlation between NfL levels obtained with MSD's R-PLEX^® and Quanterix's single molecule array (Simoa^®) assays in CSF and serum (n=32).

Results: Using the R-PLEX, NfL was quantitated in 99% of the samples tested, and showed a broad range in the CSF (82-500,000 ng/L) and serum (8.84-2,014 ng/L). Nf-L levels in both biofluids correlated strongly (r=0.81, p<0.0001). Lastly, Nf-L measured by MSD's R-PLEX and Quanterix's Simoa assays were highly correlated for both biofluids (CSF: r=0.94, p<0.0001; serum: r=0.95, p<0.0001).

Conclusions: We show that MSD's R-PLEX NfL assay can reliably quantitate levels of NfL in the CSF and serum from patients with MS and controls, where levels correlate strongly with Simoa.

Context specificity refers to the vexing phenomenon whereby a physician can see two patients with the same presenting complaint, identical history and physical examination findings, but due to specific situational (contextual) factors arrives at two different diagnostic labels. Context specificity remains incompletely understood and undoubtedly leads to unwanted variance in diagnostic outcomes. Previous empirical work has demonstrated that a variety of contextual factors impacts clinical reasoning. These findings, however, have largely focused on the individual clinician; here we broaden this work to reframe context specificity in relation to clinical reasoning by an internal medicine rounding team through the lens of Distributed Cognition (DCog). In this model, we see how meaning is distributed amongst the different members of a rounding team in a dynamic fashion that evolves over time. We describe four different ways in which context specificity plays out differently in team-based clinical care than for a single clinician. While we use examples from internal medicine, we believe that the concepts we present apply equally to other specialties and fields in health care.

Objectives: Describe medical student perspectives on framework learning and develop a free, online, mobile-friendly framework website.

Methods: Internal medicine clerkship students were surveyed at a single U.S. medical school regarding how they learn frameworks. We used Draw.io to create frameworks, which were edited by expert clinicians. Frameworks were hosted online through an academic server, and Google analytics was used to track website activity.

Results: Most medical students report learning frameworks from attending clinicians. We developed 87 frameworks on the "Penn Frameworks'' website, which was visited by 9,539 unique users from 124 countries over three years.

Conclusions: Most medical students perceive that they learn frameworks during clinical rotations from attending clinicians. We found that it is feasible to develop a low-cost, expert-curated, mobile-friendly resource to supplement in-person learning.

Pivot and cluster strategy (PCS) is a cognitive forcing strategy designed to achieve diagnostic accuracy through the analytical deployment of a cluster of differential diagnoses (Cluster) specific to the initial most likely diagnosis (Pivot) recalled by a clinical diagnostician. This approach has been widely implemented and has effectively decreased diagnostic errors. Kahneman et al. have introduced innovative notions of noise and decision hygiene. Noise refers to the variance of errors, with numerous individuals' errors in judgment pointing in different directions. They suggest a "Decision Hygiene" (DH) template, w preventative technique meant to reduce noise in decision-making. This paper introduced an interpretation of the existing strategy of PCS from new perspectives of noise and DH, which would allow us to further understand the usefulness of PCS, thereby contributing to a positive effect on the quality of diagnosis.