The stories they tell: How third year medical students portray patients,family members,physicians, and themselves in difficult encounters

Background: Physicians have long had patients whom they have labeled “difficult”, but little is known about how medical students perceive difficult encounters with patients.

Methods: In this study, we analyzed 134 third year medical students’ reflective essays written over an 18-month period about difficult student–patient encounters. We used a qualitative computerized software program, Atlas.ti to analyze students’ observations and reflections.

Results: Main findings include that students described patients who were angry and upset; noncompliant with treatment plans; discussed “nonmedical” problems; fearful, worried, withdrawn, or “disinterested” in their health. Students often described themselves as anxious, uncertain, confused, and frustrated. Nevertheless, they saw themselves behaving in empathic and patient-centered ways while also taking refuge in “standard” behaviors not necessarily appropriate to the circumstances. Students rarely mentioned receiving guidance from attendings regarding how to manage these challenging interactions.

Conclusions: These third-year medical students recognized the importance of behaving empathically in difficult situations and often did so. However, they often felt overwhelmed and frustrated, resorting to more reductive behaviors that did not match the needs of the patient. Students need more guidance from attending physicians in order to approach difficult interactions with specific problem-solving skills while maintaining an empathic, patient-centered context.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

IFN-alpha1,8 inhibits tumor-induced angiogenesis in murine angiosarcomas.

Interferon-alpha (IFN-alpha) has proved effective in the treatment of hemangiomas, hemangioblastomas, and Kaposi's sarcoma. To investigate the ability of IFNs to inhibit angiosarcoma, we used two transformed murine endothelial cell lines that form angiosarcomas in vivo. SVR and MS1-VEGF cell lines express oncogenic H-ras or vascular endothelial growth factor (VEGF), respectively. IFN-alpha1,8, which is active against murine and human cells, inhibited SVR and MS1-VEGF proliferation in vitro by 40% at 10(3) U/mL (p = 0.028). In vivo, IFN-alpha1,8 inhibited SVR tumor volume by 71% (p = 0.047) and MS1-VEGF volume by 79% (p = 0.003). Tumor-induced angiogenesis was decreased in SVR tumors by 52% (p = 0.005) and in MS1-VEGF tumors by 58% (p = 0.001). Sera from IFN-alpha1,8-treated mice bearing either SVR or MS1-VEGF tumors demonstrated a 5-fold increase in IP-10/CXCL10 (p = 0.001), an IFN-induced antiangiogenic protein. Both recombinant IP-10 and IFN-alpha1,8 inhibited human umbilical vein endothelial cell (HUVEC) vessel formation in the fibrin gel assay, a three-dimensional culture model of angiogenesis, by 56% at 25 ng/mL and 50% at 1.2 ng/mL, respectively (p < 0.001). An IP-10 blocking antibody restored vessel formation to 80% of untreated controls (p = 0.001). Given the magnitude of the in vivo response, these data suggested that the antitumor effects of IFN-alpha1,8 were likely mediated through angiogenesis inhibition rather than solely by direct inhibition of tumor cell proliferation.     

Dietary Tryptophan Reverses Alcohol-induced Impairment of Facial Recognition But Not Verbal Recall

The present study demonstrates that alcohol-induced memory impairment can be attenuated by pretreatment with an oral tryptophan supplementation. These results provide support for the role of a brain serotonin deficit in this impairment and highlight the impact a dietary manipulation can have on a complex behavioral process.