Chronic severe pain was prevalent in this predominantly employed, alcoholic population attending an outpatient drug and alcohol treatment program. Pain was associated with significant functional impairment, medical and psychiatric comorbidities, and abuse behaviors. Few patients accessed adequate pain treatment. Efforts should be made to better address the pain problems in this patient population.

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight 1 has so far served over 15K users with over 42K page views and 13% returns.

Background. DCIS treated by mastectomy ensures high local control rates. There is limited data on risk for relapse and lack of clear indication for adjuvant radiation therapy (RT). We report a retrospective review on a population of DCIS patients treated with mastectomy. The objective was to identify the overall incidence of relapse, risk factors for local recurrence, and accordingly for whom adjuvant postmastectomy RT may be considered. Methods. This is an IRB-approved retrospective study on a prospective breast cancer database. From 1997 to 2007, we identified 969 patients with diagnoses of DCIS, among them 211 breasts in 207 patients were treated with mastectomy and comprise the study group. Results. With a median followup of 55 months (4.6 years) the 10-year relapse-free survival is 97%. Two of 211 breasts (0.9%) treated with mastectomy developed a local-regional recurrence. Both the relapses were among patients defined as having <1 mm final mastectomy margin. Conclusions. The rare local relapse after mastectomy limits our ability to reliably identify risk factors for relapse. The consideration for postmastectomy RT should be based on an individualized risk evaluating surgical technique used, presence of BRCA mutation, grade and extent of tumor, and proximity of lesion to the margin of resection.

Journals play a critical role in the scientific process because they evaluate the quality of incoming papers and offer an organizing filter for search. However, the role of journals has been called into question because new preprint archives and academic search engines make it easier to find articles independent of the journals that publish them. Research on this issue is complicated by the deeply confounded relationship between article quality and journal reputation. We present an innovative proxy for individual article quality that is divorced from the journal's reputation or impact factor: the number of citations to preprints posted on http://arxiv.org. Using this measure to study three subfields of physics that were early adopters of arXiv, we show that prior estimates of the effect of journal reputation on an individual article's impact (measured by citations) are likely inflated. While we find that higher‐quality preprints in these subfields are now less likely to be published in journals compared to prior years, we find little systematic evidence that the role of journal reputation on article performance has declined.

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The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of information makes it hard for researchers to keep track of their own field, let alone explore new directions. Standard search engines are designed primarily for targeted search and are not geared for discovery or making connections that are not obvious from reading individual papers.In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research. Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets (e.g., proteins, genes, drugs, diseases, patient characteristics); and second, discovering groups of researchers and how they are connected. We extract entities using a language model pre-trained on several biomedical information extraction tasks, and enrich them with data from the Microsoft Academic Graph (MAG). To find research groups automatically, we use hierarchical clustering with overlap to allow authors, as they do, to belong to multiple groups. Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation.SciSight 1 has thus far served over 10K users with over 30K page views and 13% returning users. Preliminary user interviews with biomedical researchers suggest that SciSight complements current approaches and helps find new and relevant knowledge. * Denotes equal contribution 1

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization-the Pew Biomedical Scholars program-but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars.

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