Being the recipient of complaints is a common experience for healthcare workers. Complaints are often surprising for the novice caregiver who expects to be appreciated for services provided. In reality, most patients are grateful for the attention and assistance from caregivers. However, complaints, for a variety of reasons, are a frequent and regular occurrence in the healthcare arena. Whether caregivers are physicians, nurses, infection control practitioners, or support staff, few caregivers can avoid encountering the patient who complains.

Complaining is defined by Webster-l as an expression of pain, grief, or discomfort and a way to make an accusation or charge. While the definition may appear simple and straightforward, complaints from patients often are complex. A complaint may have a variety of meanings. The most significant meaning is frequently not apparent in the verbalized complaint. Furthermore, the verbalized complaint is frequently not the most significant concern of the patient.” For the caregiver to respond effectively, it is important to identify and understand the meaning behind the patient's complaint.

In 1970, a new papovavirus was isolated from both urine and the ureteral epithelium of a renal allograft recipient with ureteral obstruction. The virus was found to be a member of the polyoma subgroup of the Papovaviridae that was unlike any previously described. The new isolate was given the name BK virus after the initials of the renal transplant patient from whom it was cultured.

A variety of statistical tests of a null hypothesis commonly are used in biomedical studies. While these tests are the mainstay for justifying inferences drawn from data, they have important limitations. This report discusses the relative merits of two different approaches to data analysis and display, and recommends the use of confidence intervals rather than classic hypothesis testing.

Formulae for a confidence interval surrounding the point estimate of an average value take the form: d= ±zσ/√n, where “d” represents the average difference between central and extreme values, “z” is derived from the density function of a known distribution, and “a/-∨n” represents the magnitude of sampling variability. Transposition of terms yields the familiar formula for hypothesis testing of normally distributed data (without applying the finite population correction factor): z = d/(σ/√n).