18. Therapeutics: Treatment trials

1999 ◽  
Vol 36 (1-3) ◽  
pp. 269-303
Keyword(s):  
Author(s):  
Jim Fawcett ◽  
Zack Koopman ◽  
Lance Miller ◽  
Wayne Roush ◽  
Josh Sievers

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii79-ii79
Author(s):  
Kathryn Nevel ◽  
Samuel Capouch ◽  
Lisa Arnold ◽  
Katherine Peters ◽  
Nimish Mohile ◽  
...  

Abstract BACKGROUND Patients in rural communities have less access to optimal cancer care and clinical trials. For GBM, access to experimental therapies, and consideration of a clinical trial is embedded in national guidelines. Still, the availability of clinical trials to rural communities, representing 20% of the US population, has not been described. METHODS We queried ClinicalTrials.gov for glioblastoma interventional treatment trials opened between 1/2010 and 1/2020 in the United States. We created a Structured Query Language database and leveraged Google application programming interfaces (API) Places to find name and street addresses for the sites, and Google’s Geocode API to determine the county location. Counties were classified by US Department of Agriculture Rural-Urban Continuum Codes (RUCC 1–3 = urban and RUCC 4–9 = rural). We used z-ratios for rural-urban statistical comparisons. RESULTS We identified 406 interventional treatment trials for GBM at 1491 unique sites. 8.7% of unique sites were in rural settings. Rural sites opened an average of 1.7 trials/site and urban sites 2.8 trials/site from 1/2010–1/2020. Rural sites offered more phase II trials (63% vs 57%, p= 0.03) and fewer phase I trials (22% vs 28%, p= 0.01) than urban sites. Rural locations were more likely to offer federally-sponsored trials (p< 0.002). There were no investigator-initiated or single-institution trials offered at rural locations, and only 1% of industry trials were offered rurally. DISCUSSION Clinical trials for GBM were rarely open in rural areas, and were more dependent on federal funding. Clinical trials are likely difficult to access for rural patients, and this has important implications for the generalizability of research as well as how we engage the field of neuro-oncology and patient advocacy groups in improving patient access to trials. Increasing the number of clinical trials in rural locations may enable more rural patients to access and enroll in GBM studies.


1990 ◽  
Vol 31 (3) ◽  
pp. 369-371
Author(s):  
Martha J. Ulvund

2011 ◽  
Vol 57 (4) ◽  
pp. 346-354.e6 ◽  
Author(s):  
Scott E. Kasner ◽  
Jill M. Baren ◽  
Peter D. Le Roux ◽  
Pamela G. Nathanson ◽  
Katherine Lamond ◽  
...  

2010 ◽  
Vol 33 (2) ◽  
pp. 286-287
Author(s):  
A. W. Mangel ◽  
J. F. Johanson ◽  
Y.-P. Li ◽  
K. Kersey ◽  
O. Daniels

1980 ◽  
Vol 46 (3_suppl) ◽  
pp. 1139-1150 ◽  
Author(s):  
Jagdish K. Dua

Eight groups of subjects were trained to make a button-press avoidance response and were then given 15 treatment trials before being tested for extinction. Subjects in the Flooding group were exposed to the 2000-Hz tone (CS) for 20.0 sec. irrespective of their responding. In six other groups subjects were treated according to a 2 × 3 design. They were either exposed to the original CS (CS groups) or a hierarchy of tone stimuli leading to the CS (Graded CS groups). Subjects were asked either to imagine nothing (CS-only and Graded CS-only), imagine neutral events (CS-neutral and Graded CS-neutral), or imagine pleasant events (CS-pleasant and Graded CS-pleasant). A Yoked-flooding group in which CS durations were identical to the CS durations in the CS-pleasant group was also tested. Testing during extinction showed that avoidance responding was most resistant to extinction in the CS-only, Graded CS-only, and Neutral groups and least in the Flooding group. The Pleasant groups showed quicker extinction than the Neutral, CS-only, and Graded CS-only groups. The CS-pleasant group was quicker to extinguish than the Yoked-flooding group. Although flooding was the most effective procedure, the results showed that not all the effects of response competition, through imagination of pleasant experiences, could be explained through the CS-exposure hypothesis.


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