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.     

The relationship between childhood maltreatment and psychopathology in adults undergoing bariatric surgery

Background

A history of childhood maltreatment and psychopathology are common in adults with obesity.

Adolescent neighborhood quality predicts adult dACC response to social exclusion

Neuroimaging studies using the social-exclusion paradigm Cyberball indicate increased dorsal anterior cingulate cortex (dACC) and right insula activity as a function of exclusion. However, comparatively less work has been done on how social status factors may moderate this finding. This study used the Cyberball paradigm with 85 (45 females) socio-economically diverse participants from a larger longitudinal sample. We tested whether neighborhood quality during adolescence would predict subsequent neural responding to social exclusion in young adulthood. Given previous behavioral studies indicating greater social vigilance and negative evaluation as a function of lower status, we expected that lower adolescent neighborhood quality would predict greater dACC activity during exclusion at young adulthood. Our findings indicate that young adults who lived in low-quality neighborhoods in adolescence showed greater dACC activity to social exclusion than those who lived in higher quality neighborhoods. Lower neighborhood quality also predicted greater prefrontal activation in the superior frontal gyrus, dorsal medial prefrontal cortex and the middle frontal gyrus, possibly indicating greater regulatory effort. Finally, this effect was not driven by subsequent ratings of distress during exclusion. In sum, adolescent neighborhood quality appears to potentiate neural responses to social exclusion in young adulthood, effects that are independent of felt distress.