A student asks the nurse at the student health clinic how HIV is diagnosed. Which of the following statements would be the best response by the nurse?
"A diagnosis of HIV is made when CD4 T-lymphocytes drop to less than 30 cells/mm."
"A diagnosis of HIV is made when antibodies to HIV reach peak levels of 1000/ml of blood."
"A diagnosis of HIV is made when a patient tests positive for Syphilis."
"A diagnosis of HIV is made when antibodies to HIV are detected (seroconversion) about 6 weeks to 3 months following possible exposure."
The Correct Answer is D
Choice A reason: CD4 drop is AIDS, not HIV diagnosis. Seroconversion fits, per nursing. This errors in stage. It’s universally distinct.
Choice B reason: Antibody levels aren’t quantified at 1000/ml for diagnosis. Seroconversion is correct, per standards. This misaligns with facts. It’s universally distinct.
Choice C reason: Syphilis is unrelated; HIV diagnosis uses antibodies. This errors per nursing knowledge. It’s universally distinct, wrong disease.
Choice D reason: HIV diagnosis detects antibodies during seroconversion, 6 weeks to 3 months. This aligns with nursing standards. It’s universally accurate, distinctly true.
Nursing Test Bank
Naxlex Comprehensive Predictor Exams
Related Questions
Correct Answer is A
Explanation
Choice A reason: Epi-pen treats allergic reaction, a tertiary prevention step. This fits public health standards. It’s universally recognized, distinctly post-event care.
Choice B reason: Assurance is a function, not prevention; tertiary fits. This errors per nursing definitions. It’s universally distinct, not a level.
Choice C reason: Primary prevents allergies; epi-pen manages them. This misaligns with prevention levels. It’s universally distinct, pre-exposure focus.
Choice D reason: Secondary screens; epi-pen treats existing conditions. This errors per nursing standards. It’s universally distinct, not detection.
Correct Answer is A
Explanation
Choice A reason: Prevalence measures all cases (new and existing) of a disease in a population at a given time. This formula captures total burden, like diabetes cases, divided by population, adjusted by a base (e.g., 1000), reflecting overall disease presence accurately.
Choice B reason: This formula calculates sensitivity, not prevalence. It’s used in diagnostics to assess true positive rates for diseases like cancer against missed cases, focusing on test accuracy, not the total number of affected individuals in a population over time.
Choice C reason: This represents specificity, evaluating true negatives in diagnostic testing, not prevalence. It’s relevant for ruling out disease, like tuberculosis, but doesn’t quantify how many people currently have it within a population, missing the broader epidemiological scope entirely.
Choice D reason: This defines incidence, not prevalence, counting only new cases over time, like annual flu cases. It excludes existing cases, underrepresenting the total disease load in a population, which prevalence aims to capture comprehensively for health planning.
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