Participants in phase 1 oncology research trials - Are they vulnerable?
ABSTRACT Phase 1 oncology trials involve risk and offer a relatively low prospect of benefit to participants. Some claim that participants constitute a vulnerable population requiring special protections. We undertook this study to determine whether phase 1 oncology trial participants have demographic and health status characteristics of a vulnerable population. We reviewed participant demographic and health status data from phase 1 trials sponsored by the Cancer Therapy Evaluation Program at the National Cancer Institute that began between 1991 and 2002 and from 11 previously published studies. Main outcome measures were median age, sex, race/ethnicity, performance status, previous therapy, educational achievement level, and health insurance coverage. Almost 10 000 participants in trials sponsored by the Cancer Therapy Evaluation Program had a median age of 57 years, 90% self-identified as white, 93% had near-normal performance status, 85% had some form of health insurance, and 92% had been previously treated for cancer; 20 000 individuals from published studies had comparable profiles. The demographic and health status characteristics of phase 1 oncology trial participants are not those of a conventional vulnerable population and suggest little reason to assume that, as a group, they have a compromised ability to understand information or to make informed and voluntary decisions.
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ABSTRACT: MEDICATION SAFETY Overlooked Renal Dosage Adjustments A retrospective analysis of 647 patients at hospital discharge com-pared required renal dosage adjust-ments to dosage actually prescribed. This study was conducted at VieCuri Medical Centre in Venlo, Netherlands. Patient demographics and renal function data were col-lected, and dosage adjustment needs were assessed via the pharmacy-supported discharge counseling ser-vice. The incidence of inappropriate dosing based on renal function was measured at hospital discharge. Thirty-seven percent of patients evaluated during the study period (237/647) had a creatinine clear-ance less than 51 mL/min/1.73 m 2 ; dosage adjustment was warranted in 23.9% (411/1,718) of prescrip-tions. When dosage adjustment should have been performed, more than 40% of prescriptions (169/411; 41.1%) were inappropri-ate for renal function (9.8% of pre-scriptions overall; 169/1,718). Fur-thermore, 60.4% (102/169) of inappropriate prescriptions pos-sessed the potential for moderate or severe clinical consequences, as evaluated by a panel of two clinical pharmacologists and one nephrolo-gist. Study authors also noted a lack of standardized dosing guidelines for agents requiring renal dosage adjustment. The authors also sug-gested that augmenting medication systems by adding dynamic renal dosing alerts would improve moni-toring. Summary: A comparison of suggested renal dosing and actual dosing at hospital discharge revealed that appropriate prescribing may be overlooked. van Dijk EA, Drabbe NRG, Kruijtbosch M, De Smet PAGM. Drug dosage adjust-ments according to renal function at hos-pital discharge. Ann Pharmacother. 2006;40:1254-1260.Hospital pharmacy 12/1122; 41. DOI:10.1310/hpj4311-937
Conference Paper: 3-D Perspective View Of Sonar ImagesSignals, Systems and Computers, 1988. Twenty-Second Asilomar Conference on; 02/1988
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ABSTRACT: The paper describes the self-organizing wavelet neural network (SOWNN) for nonlinear time-varying channel equalizers. The SOWNN model has a four-layer structure which is comprised of an input layer, a wavelet layer, a product layer and an output layer. The derivative online learning algorithm involves two kinds of learning. The structure learning is performed to determine the network structure and the parameter learning is to adjust the shape of the wavelet bases and the connection weights of a SOWNN. The proposed equalizer is enhanced in order to handle the highly nonlinear functionality. Computer simulation results show that the bit error rate of the SOWNN equalizer is very close to that of the optimal equalizer.Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on; 08/2004