Do Physicians Know When Their Diagnoses Are Correct?

This study explores the alignment between physicians' confidence in their diagnoses and the “correctness” of these diagnoses, as a function of clinical experience, and whether subjects were prone to over-or underconfidence. Design : Prospective, counterbalanced experimental design. Setting : Laboratory study conducted under controlled conditions at three academic medical centers. Participants : Seventy-two senior medical students, 72 senior medical residents, and 72 faculty internists. Intervention : We created highly detailed, 2-to 4-page synopses of 36 diagnostically challenging medical cases, each with a definitive correct diagnosis. Subjects generated a differential diagnosis for each of 9 assigned cases, and indicated their level of confidence in each diagnosis. Measurements And Main Results : A differential was considered “correct” if the clinically true diagnosis was listed in that subject's hypothesis list. To assess confidence, subjects rated the likelihood that they would, at the time they generated the differential, seek assistance in reaching a diagnosis. Subjects' confidence and correctness were “mildly” aligned (Κ=.314 for all subjects, .285 for faculty, .227 for residents, and .349 for students). Residents were overconfident in 41% of cases where their confidence and correctness were not aligned, whereas faculty were overconfident in 36% of such cases and students in 25%. Conclusions : Even experienced clinicians may be unaware of the correctness of their diagnoses at the time they make them. Medical decision support systems, and other interventions designed to reduce medical errors, cannot rely exclusively on clinicians' perceptions of their needs for such support.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74850/1/j.1525-1497.2005.30145.x.pd

Constructing Biological Pathways by a Two-Step Counting Approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network

Overexpression of Akt1 Enhances Adipogenesis and Leads to Lipoma Formation in Zebrafish

<div><h3>Background</h3><p>Obesity is a complex, multifactorial disorder influenced by the interaction of genetic, epigenetic, and environmental factors. Obesity increases the risk of contracting many chronic diseases or metabolic syndrome. Researchers have established several mammalian models of obesity to study its underlying mechanism. However, a lower vertebrate model for conveniently performing drug screening against obesity remains elusive. The specific aim of this study was to create a zebrafish obesity model by over expressing the insulin signaling hub of the <em>Akt1</em> gene.</p> <h3>Methodology/Principal Findings</h3><p><em>Skin oncogenic transformation screening shows that a stable zebrafish transgenic of Tg(krt4Hsa.myrAkt1</em>)^cy18 displays severely obese phenotypes at the adult stage. In Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18, the expression of exogenous human constitutively active Akt1 (myrAkt1) can activate endogenous downstream targets of mTOR, GSK-3α/β, and 70S6K. During the embryonic to larval transitory phase, the specific over expression of myrAkt1 in skin can promote hypertrophic and hyperplastic growth. From 21 hour post-fertilization (hpf) onwards, myrAkt1 transgene was ectopically expressed in several mesenchymal derived tissues. This may be the result of the integration position effect. Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18 caused a rapid increase of body weight, hyperplastic growth of adipocytes, abnormal accumulation of fat tissues, and blood glucose intolerance at the adult stage. Real-time RT-PCR analysis showed the majority of key genes on regulating adipogenesis, adipocytokine, and inflammation are highly upregulated in Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18. In contrast, the myogenesis- and skeletogenesis-related gene transcripts are significantly downregulated in Tg(<em>krt4:Hsa.myrAkt1</em>)^cy18, suggesting that excess adipocyte differentiation occurs at the expense of other mesenchymal derived tissues.</p> <h3>Conclusion/Significance</h3><p>Collectively, the findings of this study provide direct evidence that Akt1 signaling plays an important role in balancing normal levels of fat tissue in vivo. The obese zebrafish examined in this study could be a new powerful model to screen novel drugs for the treatment of human obesity.</p> </div

Randomized Controlled Trial of an Informatics-based Intervention to Increase Statin Prescription for Secondary Prevention of Coronary Disease

OBJECTIVE: Suboptimal treatment of hyperlipidemia in patients with coronary artery disease (CAD) is well documented. We report the impact of a computer-assisted physician-directed intervention to improve secondary prevention of hyperlipidemia. DESIGN AND SETTING: Two hundred thirty-five patients under the care of 14 primary care physicians in an academically affiliated practice with an electronic health record were enrolled in this proof-of-concept physician-blinded randomized, controlled trial. Each patient with CAD or risk equivalent above National Cholesterol Education Program-recommended low-density lipoprotein (LDL) treatment goal for greater than 6 months was randomized, stratified by physician and baseline LDL. Physicians received a single e-mail per intervention patient. E-mails were visit independent, provided decision support, and facilitated “one-click” order writing. MEASUREMENTS: The primary outcomes were changes in hyperlipidemia prescriptions, time to prescription change, and changes in LDL levels. The time spent using the system was assessed among intervention patients. RESULTS: A greater proportion of intervention patients had prescription changes at 1 month (15.3% vs 2%, P=.001) and 1 year (24.6% vs 17.1%, P=.14). The median interval to first medication adjustment occurred earlier among intervention patients (0 vs 7.1 months, P=.005). Among patients with baseline LDLs >130 mg/dL, the first postintervention LDLs were substantially lower in the intervention group (119.0 vs 138.0 mg/dL, P=.04). Physician processing time was under 60 seconds per e-mail. CONCLUSION: A visit-independent disease management tool resulted in significant improvement in secondary prevention of hyperlipidemia at 1-month postintervention and showed a trend toward improvement at 1 year