Gender bias in diagnosing fibromyalgia.

BACKGROUND: Both patient- and physician-centered characteristics may influence disease classification c fibromyalgia (FM). OBJECTIVE: This study assessed the diagnostic criteria for FM and how rheumatologists use these criter in clinical practice. METHODS: Practicing rheumatologists were surveyed. Participants were asked to read a brief case description of a patient with FM and then to select those criteria most important to them for confirming tt diagnosis. Case studies of either male or female patients were randomly assigned. Data were analyzed using a random forests classification analysis to abstract the strongest variables for distinguishing disease classification--in this assessment, stratified by gender of the case study. RESULTS: A total of 61 surveys were analyzed. Four rheumatologists (6.6%) chose the 2 (and only the 2 criteria for FM classification (tender points and widespread pain) proposed by the American College of Rheumatology (ACR). The candidate diagnostic criteria discriminating between rheumatologists (when stratified by gender of the case study) consisted of (1) tender points, (2) normal erythrocyte sedimentatio rate, (3) normal thyroid tests, (4) fatigue, and (5) poor quality of sleep. Of these, the criterion of tender points was chosen by rheumatologists statistically more frequently for male patients (P = 0.047). CONCLUSIONS: This study provides insight into the diagnostic thought processes of rheumatologists. minority of practitioners relied solely on the published ACR classification criteria for the diagnosis of FM. We also report gender bias with regard to disease classification, because rheumatologists were more likely to require a physical finding to support a diagnostic conclusion in male patients.

Random forests classification analysis for the assessment of diagnostic skill.

Mechanisms are needed to assess learning in the context of graduate medical education. In general, research in this regard is focused on the individual learner. At the level of the group, learning assessment can also inform practice-based learning and may provide the foundation for whole systems improvement. The authors present the results of a random forests classification analysis of the diagnostic skill of rheumatology trainees as compared with rheumatology attendings. A random forests classification analysis is a novel statistical approach that captures the strength of alignment of thinking between student and teacher. It accomplishes this by providing information about the strength and correlation of multiple variables.