PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

The impact of neighborhoods and friendships on interracial anxiety among medical students and residents: A report from the medical student CHANGES study

Objective

To examine the experience of interracial anxiety among health professionals and how it may affect the quality of their interactions with patients from racially marginalized populations. We explored the influence of prior interracial exposure—specifically through childhood neighborhoods, college student bodies, and friend groups—on interracial anxiety among medical students and residents. We also examined whether levels of interracial anxiety change from medical school through residency.

Data Source

Web-based longitudinal survey data from the Medical Student Cognitive Habits and Growth Evaluation Study.

Study Design

We used a retrospective longitudinal design with four observations for each trainee. The study population consisted of non-Black US medical trainees surveyed in their 1st and 4th years of medical school and 2nd and 3rd years of residency. Mixed effects longitudinal models were used to assess predictors of interracial anxiety and assess changes in interracial anxiety scores over time.

Principal Findings

In total, 3155 non-Black medical trainees were followed for 7 years. Seventy-eight percent grew up in predominantly White neighborhoods. Living in predominantly White neighborhoods and having less racially diverse friends were associated with higher levels of interracial anxiety among medical trainees. Trainees' interracial anxiety scores did not substantially change over time; interracial anxiety was highest in the 1st year of medical school, lowest in the 4th year, and increased slightly during residency.

Conclusions

Neighborhood and friend group composition had independent effects on interracial anxiety, indicating that premedical racial socialization may affect medical trainees' preparedness to interact effectively with diverse patient populations. Additionally, the lack of substantial change in interracial anxiety throughout medical training suggests the importance of providing curricular tools and structure (e.g., instituting interracial cooperative learning activities) to foster the development of healthy interracial relationships.     

The use of cadaver models to diagnose rib fractures: A pilot study

Background

In the emergency department, rib fractures are a common finding in patients who sustain chest trauma. Rib fractures may be a sign of significant, underlying pathology, especially in the elderly patients where rib fractures are associated with significant morbidity and mortality. To date, no studies have evaluated the ability of ultrasound to detect rib fractures using cadaver models and subsequently use this model as a teaching tool.