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Table 1 Net sensitivity and specificity of diagnostic algorithms reflected in each scenario, by smear type. Green cells represent large increase in value compared to baseline algorithm

From: Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling

Scenario Definition Net sensitivity Net specificity
Smear positive Smear negative
Baseline Prolonged cough & microscopy/clinical diagnosis 50.0% 20.9% 94.9%
Scenario 1 Algorithm A Prolonged cough & GeneXpert 49.1% 27.8%* 99.9%*
Algorithm B Any symptom & microscopy/clinical diagnosis 77.0%* 20.9% 94.3%
Scenario 2 Microscopy (baseline) Prolonged cough & microscopy/clinical diagnosis 50.0% 20.9% 94.9%
GeneXpert Prolonged cough & GeneXpert 49.1% 27.8%* 99.9%*
  1. *Asterisk signifies large increase in value compared to baseline algorithm; Scenario 1 comparing the impact of two different diagnostic algorithms in a defined population; Scenario 2 examining the impact of expanding case detection towards population of lower disease
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