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[This corrects the article DOI: 10.1371/journal.pone.0312540.].

\"U.S. News exclusive survey.\"

[This corrects the article DOI: 10.1371/journal.pone.0257449.].

Background Postgraduate medical education in oncology orthopedics confronts obstacles when instructing on pelvic tumors, primarily due to their intricate anatomy and the limitations of conventional teaching techniques. The employment of Three-dimensional multimodality imaging (3DMMI) can be considered a valuable teaching tool, as it gracefully elucidates the intricacies of pelvic anatomical structures and the interactions between tumors and surrounding tissues through three-dimensional imaging, thereby providing a comprehensive and nuanced perspective. This study aimed to assess the feasibility and effectiveness of incorporating 3DMMI in combination with a Case-Based Learning (CBL) approach for postgraduate education. Methods The study encompassed a 10-week course involving 90 surgical postgraduates, focusing on common pelvic tumor diseases. Students were assigned representative clinical cases, and each group created a PowerPoint presentation based on these cases. The core educational content included fundamental knowledge of pelvic anatomy, as well as clinical presentations, radiological features, and treatment principles of common pelvic tumor diseases. The research compared two groups: a traditional CBL group (n = 45) and a 3DMMI-CBL group (n = 45). The 3DMMI-CBL group had access to advanced imaging technology for better visualization. Various evaluations, including image interpretation, theoretical knowledge, and questionnaires, were used to assess the learning outcomes. Results The 3DMMI-CBL group outperformed the CBL group not only in the imaging diagnosis of common pelvic diseases but also in their mastery of the related theoretical knowledge. Student questionnaires indicated higher scores for the 3DMMI-CBL group in basic pelvic anatomy knowledge (8.08 vs. 6.62, p  < 0.01), image interpretation (8.15 vs. 6.69, p  < 0.01), learning efficiency (8.07 vs. 7.00, p  < 0.01), clinical reasoning (7.57 vs. 6.77, p  < 0.01), and learning interest (8.46 vs. 7.00, p  < 0.01). Teacher questionnaires revealed that 3DMMI technology enhanced teachers’ clinical knowledge, facilitated instruction, and increased overall satisfaction and interest in teaching. Conclusion Our study introduced an enhancement to the conventional Case-Based Learning (CBL) model by incorporating 3DMMI technology for visualizing pelvic anatomy. In contrast to pure CBL, this adaptation improved teacher instruction, substantially heightened student engagement, ignited greater interest in learning, and boosted overall efficiency, ultimately leading to positive learning outcomes. Consequently, our study demonstrated the potential feasibility and acceptability of the 3DMMI-CBL teaching method for postgraduates in pelvic bone tumor education.

\"After completing the medical school application comes the last and often most challenging aspect of the school selection process: the interview. Notoriously hard to prepare for, it's difficult to know what questions might be asked and how to answer them.Extensively revised, How to Succeed In Your Medical School Interview de-mystifies the interview process. It provides a systematic and methodical process which enables the interviewee to mine information from examiners, while demonstrating academic ability. Full of practice questions and free downloadable podcasts of mock interviews, this book offers tips on preparation, presentation, and most importantly, what to say. The most significant addition to the book covers the multiple mini-interview system, which schools are beginning to use instead of the formal, fixed panel interview. This new system is made up of short \"stations\" ranging from 3-10 minutes, with a specific goal and a separate interviewer. The format can be a conventional interview question, a role play, linguistic skill test, writing exercise, or another challenge. The author has also added more graph-and-table data interpretation questions to the Oxbridge interview section and updated discussion material to include the current \"hot topics\" in medicine, such as e-cigarettes, medical ethics, and the US patent ban on genes. \"-- Provided by publisher.

Background Medical school acceptance rates in the United States (US) have been lower for applicants who identify as Underrepresented-in-Medicine (UiM) compared to non-UiM applicants. The gap between UiM and no-UiM groups is narrowing in recent years. Less well-studied are associations of acceptance decisions with family income and parental education. This study’s purpose is to evaluate the relationships between medical school acceptance and family income, parental education status, racial/ethnic background, Grade Point Average (GPA), Medical College Admission Test (MCAT) score, and participation in extracurricular activities. Methods This is a cross-sectional study of first-time US medical school applicants between 2017 and 2020. Acceptance rates for first-time applicants were calculated for first-generation (FG), low-income (LI), and UiM applicants. Associations of these attributes with MCAT scores, science GPAs, and seven categories of extracurricular activities were evaluated. Regression analyses estimated associations between acceptance to medical school with all variables with and without interaction terms (FG*URM, LI*URM, FG*LI). Results The overall acceptance rate for first-time applicants from 2017–2020 was 45.3%. The acceptance rates among FG, LI and UiM applicants were 37.9%, 39.6% and 44.2%, respectively. In univariable logistic regression analyses, acceptance was negatively associated with being FG (OR: 0.68, CI: 0.67–0.70), LI (OR: 0.70, CI: 0.69–0.72), and UiM (OR: 0.95, CI: 0.93–0.97). In multivariable regression, acceptance was most strongly associated with science GPA (OR: 7.15, CI: 6.78–7.54 for the highest quintile) and UiM (OR: 5.56, CI: 5.48–5.93) status and MCAT score (OR: 1.19, CI: 1.18–1.19), FG (OR: 1.14, CI: 1.10–1.18), and most extracurricular activities. Including interaction terms revealed a negative association between acceptance and LI (OR:0.90, CI: 0.87–0.94) and FG was no longer significant (OR:1.10, CI:0.96–1.08). Conclusions Collectively these results suggest medical school admissions committees may be relying on holistic admission practices. While MCAT and GPA scores continue to predict acceptance, individuals from racially and ethnically UiM backgrounds have favorable odds of acceptance when controlling for MCAT and GPA. However, these positive associations were not seen for low-income and first-generation applicants. Additional preparation for college and the MCAT for these latter groups may help further diversify the medical profession.