Radiofrequency Ablation of the Infrapatellar Branch of the Saphenous Nerve for the Treatment of Chronic Anterior Inferomedial Knee Pain.

INTRO: Genicular nerve radiofrequency ablation (GNRFA) is an effective treatment for chronic knee pain related to osteoarthritis. It is often utilized when conservative management has failed and patients wish to avoid arthroplasty, are poor surgical candidates due to comorbid medical conditions, or in those suffering from persistent pain after arthroplasty. The classic targets for GNRFA include the superior lateral genicular nerve, superior medial genicular nerve, and inferior medial genicular nerve but multiple anatomic studies have demonstrated additional sensory innervation to the knee. OBJECTIVE: In this research article, we propose an image-guided technique that can safely target the infrapatellar branch of the saphenous nerve which also provides sensory innervation to the anterior capsule. PROPOSAL: The proposed technique includes variations for conventional bipolar radiofrequency ablation, cooled radiofrequency ablation, dual-tined bipolar radiofrequency ablation, and monopolar radiofrequency ablation using a long axis approach. The described technique is based on updated anatomic studies and takes into account safety concerns such as thermal risk to the skin and/or pes anserine tendons and breaching of the synovial cavity. CONCLUSION: Future clinical research should be performed to confirm the safety and effectiveness of this specific approach.

NCI's Physician Data Query (PDQ®) cancer information summaries: history, editorial processes, influence, and reach.

In the National Cancer Act of 1971, the Director of the National Cancer Institute (NCI) was given a mandate to "Collect, analyze, and disseminate all data useful in the prevention, diagnosis, and treatment of cancer, including the establishment of an International Cancer Research Data Bank (ICRDB) to collect, catalog, store, and disseminate insofar as feasible the results of cancer research undertaken in any country for the use of any person involved in cancer research in any country" (National Cancer Act of 1971, S 1828, 92nd Congress, 1st Sess (1971)). In subsequent legislation, the audience for NCI's information dissemination activities was expanded to include physicians and other healthcare professionals, patients and their families, and the general public, in addition to cancer researchers. The Institute's response to these legislative requirements was to create what is now known as the Physician Data Query (PDQ®) cancer information database. From its beginnings in 1977 as a database of NCI-sponsored cancer clinical trials, PDQ has grown to include extensive information about cancer treatment, screening, prevention, supportive and palliative care, genetics, drugs, and more. Herein, we describe the history, editorial processes, influence, and global reach of one component of the PDQ database, namely its evidence-based cancer information summaries for health professionals. These summaries are widely recognized as important cancer information and education resources, and they further serve as foundational documents for the development of other cancer information products by NCI and other organizations.

The role of user input in shaping online information from the National Cancer Institute.

The National Cancer Institute (NCI) was among the first federal agencies to recognize the potential of the Internet for disseminating health-related information. The evolution and refinement of NCI's online cancer information has been substantially "user driven"-from the launch of CancerNet in 1995 to the recent redesign of its award-winning successor, the NCI website. This article presents an overview of NCI's multi-pronged approach to gathering input about its online information products, including stakeholder meetings, focus groups, standard and customized online user surveys, usability testing, heuristic reviews, and search log analysis. Also highlighted are some of the many enhancements that have been made to NCI's online cancer information products based on user input.