Search

Parry Romberg Syndrome (PRS) and en coup de sabre (ECDS) are head variants of linear morphea with functional and structural implications. This study describes the clinical course, autoimmune co-morbidities, complications, and treatment of adults with PRS/ECDS at a tertiary referral center. We retrospectively reviewed the records of all 34 adult patients with PRS/ECDS identified through billing code search and seen by dermatologists at our institution between 2015 and 2021. Eight patients (23.5%) had ECDS, 8 (23.5%) had PRS, and 18 (52.9%) had overlap. Twenty-six patients (76.5%) reported ocular, oral, and/or neurologic symptoms, and 8 (23.5%) had concomitant autoimmune/inflammatory conditions. Sixteen patients (47.1%) had a skin biopsy, and 25 (73.5%) had imaging. Forty-six MRIs were obtained, of which 6 (13.0%) reported intracranial findings and 25 (54.3%) reported disease-related connective tissue damage. Twenty-four patients (70.6%) underwent systemic treatment during their disease course per available clinical records. Seventeen patients (70.8%) had improved or stable disease upon treatment completion, with an average duration of 22.2 months. Ten patients (41.7%) reported recurrence of disease following the treatment course. To address changes to facial contour, 6 patients (17.6%) opted for procedural treatments. One patient (16.7%) experienced morphea reactivation following a filler injection performed off-immunosuppression. Compared to findings in children, our study suggests adults with PRS/ECDS are more likely to have oral and ocular complications but experience less severe neurologic symptoms. While systemic treatments appear beneficial in most adult patients with PRS/ECDS, disease may recur following discontinuation.

Background: Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation. Objective: Our goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naive participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction. Methods: Two experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty. Results: One of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts). Conclusions: The results suggest that nonexpert annotation might be a feasible option for comprehensive labeling of annotated PDDIs across a broader range of drug product labels. Preannotation of drug mentions may ease the annotation task. However, preannotation of PDDIs, as operationalized in this study, presented the participants with difficulties. Future work should test if these issues can be addressed by the use of better performing NLP and a different approach to presenting the PDDI preannotations to users during the annotation workflow.