These results suggest that 2 tests, taking less than 1 minute, can indicate a probable diagnosis of FM in a chronic pain patient. In the case of a positive screen, a follow-up examination is required for confirmation or refutation.

Rationale, aims and objectives: The primary purpose of this study was to test both classic and novel FM pain and non-pain symptoms to determine their practical efficacy in aiding clinicians to distinguish FM pain from other chronic pain disorders.Methods: 158 pain patients from two primary care clinics were evaluated with history, physical exam, chart review, and a questionnaire containing 26 exploratory symptoms (10 from the Symptom Impact Questionnaire (SIQR) and 16 from the FM literature)). The symptoms were rated on a 0-10 VAS for severity by those patients reporting pain over the past week. Somers' D and mean severity differences between FM and chronic pain patients without FM were used to rank the discriminatory and diagnostic contributions of symptoms.Results: Fifty patients (14.2%) carried a chart diagnosis of FM, 108 (30.7%) had pain but not FM, and 192 (54.5%) who had neither pain nor FM. Comparing means between the two pain groups, the 5 best differentiating symptoms (all, P < .0001) were: a persistent deep aching over most of my body, poor balance (7.4 vs 3.1), environmental sensitivity (6.8 vs 3.0), tenderness to touch (6.8 vs 3.6) and pain after exercise (8.1 vs 4.1). Notably, VAS pain though significantly higher for FM was least discriminatory (6.5 vs 5.1, P < .001). The five best symptoms generated a ROC = 0.85 and Somers' D = 0.69, an accuracy of 81%, and an odd's ratio of 14.4. Conclusions:Our results herein suggest that clinicians may be well-served to consider symptoms in addition to those contained in current diagnostic criteria when recognizing FM in their chronic pain patients.

Background: Few quantitative studies have documented the types of research topics most commonly employed by nursing PhD students and whether they differ by program delivery (in-person vs. online/hybrid programs).Objectives: We examined a large set of publicly available PhD dissertation abstracts to (a) describe the relative prevalence of different research topics and methods and (b) test whether the primary topics and methods used differed between online or hybrid and in-person PhD programs. A secondary goal was to introduce the reader to modern text-mining approaches to generate insights from a document corpus.Methods: Our database consisted of 2,027 dissertation abstracts published between 2015 and 2019. We used a structural topic modeling text-mining approach to explore PhD students' research topics and methods in United States-based doctoral nursing programs.Results: We identified 24 different research topics representing a wide range of research activities. Most of the research topics identified did not differ in prevalence between online/hybrid and in-person programs. However, online/hybrid programs were more likely to engage students in research focused on nursing education, professional development, work environment, simulation, and qualitative analysis. Pediatrics, sleep science, older adults and aging, and chronic disease management were more prevalent topics in in-person-only programs.Discussion: The range of topics identified highlights the breadth of research nursing PhD students' conduct. Both in-person and online/hybrid programs offer a range of research opportunities, although we did observe some differences in topic prevalence. These differences could be due to the nature of some types of research (e.g., research that requires an in-person presence) or differences in research intensity between programs (e.g., amount of grant funding or proximity to a medical center). Future research should explore why research topic prevalence may vary by program delivery. We hope that this text-mining application serves as an illustrative example for researchers considering how to draw inferences from large sets of text documents. We are particularly interested in seeing future work that might combine traditional qualitative approaches and large-scale text mining to leverage the advantages of each.