Feedback is an essential element in performance training. However, little effort has been made to measure the effects of positive and negative feedback on the ability of self-rated assessment, affective responses, and motivation to learn in healthcare education. This study was a quasi-experimental posttest design to examine the effects of an examiner’s positive and negative verbal feedback on the accuracy of self-assessment, emotional responses, and self-efficacy. Second-year nursing students were recruited in a university in South Korea. A total of 110 participants were assigned randomly to a positive feedback (PF) group (n = 58) and a negative feedback (NF) group (n = 52). All participants completed the performance measure and then received a positive or negative feedback from an evaluator. After delivery of feedback, they assessed their own performance using the same sheet as the evaluator’s and completed the survey for emotional response and self-efficacy. Chi-squared tests, Fisher’s exact tests, independent sample Student’s t tests, and Mann–Whitney nonparametric U tests, and Analysis of covariance (ANCOVA) were used to compare the baseline measurements of the demographic characteristics and the dependent variables between the PF and NF groups. The NF group demonstrated a more accurate self-rated assessment than the PF group (p < 0.001). While self-efficacy (p < 0.001) and positive emotions (p < 0.001) were significantly stronger in the PF group than in the NF group, negative emotions were significantly stronger in the NF group than in the PF group (p = 0.001). Evaluator’s verbal feedback exerts a significant influence on the accuracy of self-assessment as well as emotions and self-efficacy. Instructors should pay attention to providing feedback to students, taking into account the impact of positive or negative feedback.

Hyun Suk Yang, M.D., Ph.D., Mina Hur, M.D., Ph.D., Kyeong Ryong Lee, M.D., Ph.D., Hanah Kim, M.D., Ph.D., Hahn Young Kim, M.D., Ph.D., Jong Won Kim, M.D., Ph.D., Mui Teng Chua, M.D., Win Sen Kuan, M.D., Horng Ruey Chua, M.D., Chagriya Kitiyakara, M.D., Phatthranit Phattharapornjaroen, M.D., Anchalee Chittamma, Ph.D., Thiyapha Werayachankul, M.Sc., Urmila Anandh, M.D., Sanjeeva Herath, M.B., Ch.B., Zoltan Endre, M.D., Ph.D., Andrea Rita Horvath, M.D., Ph.D., Paola Antonini, M.D., and Salvatore Di Somma, M.D., Ph.D. on behalf of the GREAT Network. Ann Lab Med 2022;42:178-87. https://doi.org/10.3343/alm.2022.42.2.178

Large language models (LLMs) have been deployed in diverse fields, and the potential for their application in medicine has been explored through numerous studies. This study aimed to evaluate and compare the performance of ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard for the Emergency Medicine Board Examination question bank in the Korean language. Of the 2353 questions in the question bank, 150 questions were randomly selected, and 27 containing figures were excluded. Questions that required abilities such as analysis, creative thinking, evaluation, and synthesis were classified as higher-order questions, and those that required only recall, memory, and factual information in response were classified as lower-order questions. The answers and explanations obtained by inputting the 123 questions into the LLMs were analyzed and compared. ChatGPT-4 (75.6%) and Bing Chat (70.7%) showed higher correct response rates than ChatGPT-3.5 (56.9%) and Bard (51.2%). ChatGPT-4 showed the highest correct response rate for the higher-order questions at 76.5%, and Bard and Bing Chat showed the highest rate for the lower-order questions at 71.4%. The appropriateness of the explanation for the answer was significantly higher for ChatGPT-4 and Bing Chat than for ChatGPT-3.5 and Bard (75.6%, 68.3%, 52.8%, and 50.4%, respectively). ChatGPT-4 and Bing Chat outperformed ChatGPT-3.5 and Bard in answering a random selection of Emergency Medicine Board Examination questions in the Korean language.

Jong Won Kim, M.D., Ph.D., Hanah Kim, M.D., Ph.D., Yeo-Min Yun, M.D., Ph.D., Kyeong Ryong Lee, M.D., Ph.D., and Hyun Joong Kim, M.D., Ph.D.. Ann Lab Med 2020;40:474-80. https://doi.org/10.3343/alm.2020.40.6.474

We demonstrated that upregulation of both gene expression of endoplasmic reticulum (ER) stress chaperones (BiP, calnexin, calreticulin, and PDI) and ER stress sensors (ATF6, IRE1 and PERK) was induced by lidocaine, a local anesthetic, in PC12 cells. In addition to gene regulation, lidocaine also induced typical ER stress phenomena such as ART6 proteolytic cleavage, eIF2 alpha phosphorylation, and XBP1 mRNA splicing. In in vivo experiments, while lidocaine downregulated gene expression of anti-apoptotic factors (Bcl-2 and Bcl-xl), pro-apoptotic factor (Bak and Bax) gene expression was upregulated. Furthermore, lidocaine induced apoptosis, as measured histochemically, and upregulated PARP1, a DNA damage repair enzyme. These results are the first to show that lidocaine induces apoptosis through ER stress in vitro and in vivo.

Background and Objectives: Presepsin (PSS) is an independent predictor for estimating disease severity and prognosis in septic patients. Few studies have reported the associations between plasma PSS and the severity and prognosis in patients with community-acquired pneumonia (CAP). We investigated whether a high plasma PSS level was associated with 30-day mortality in CAP patients. Materials and Methods: This retrospective single-center study was conducted in an emergency department. The PSS level was measured in 211 adult CAP patients admitted to the hospital and followed for up to 30 days. We recorded the pneumonia severity index (PSI) and the CURB-65 score. The primary outcome was death from any cause within 30 days. Results: The plasma PSS levels were significantly elevated in the high-risk group (PSI > 130) compared with the low- (PSI < 91) or moderate-risk groups (PSI 91–130). Forty-four patients (20.9%) died within 30 days of admission. Non-survivors had significantly higher plasma PSS levels than survivors among CAP patients: 1083 (697–1736) pg/mL vs. 385 (245–554) pg/mL (p < 0.001). The area under the curve (AUC) to predict 30-day mortality was highest for PSS (0.867), followed by procalcitonin (0.728) and lactate (0.616). The cutoff level of plasma PSS for 30-day mortality was >754 pg/mL. The combination of PSI and plasma PSS level improved the predictive ability for 30-day mortality (AUC = 0.892). Cox regression analysis showed that higher PSS levels (>754 pg/mL) and higher PSI (>126) were associated with 30-day mortality in CAP patients (hazard ratios of 19.472 and 6.375, respectively). Conclusion: Elevated plasma PSS is associated with severity and 30-day mortality in hospitalized CAP patients. Combining plasma PSS level and PSI could significantly improve the predictive ability of PSS for 30-day mortality.