|
T1- Base case
|
No triage
| | | |
|
T2- Cough 1 week
|
Respiratory symptom of cough > 1 week [18]
|
88%
|
19%
|
US$0
|
|
T3 Cough 3 weeks
|
Respiratory symptom of cough > 3 weeks [18]
|
61%
|
51%
|
US$0
|
|
T4- Clinical Score
|
Scorecard based on aggregating scores assigned to respiratory symptoms including chest pain, cough, sputum expectoration, hemoptysis, night sweats, fever, shortness of breath and weight loss [18].
|
83%
|
52%
|
US$2
|
|
T5- ANN
|
Artificial Neural Network (ANN) based on using a multilayer perceptron (MLP) approach [19] to infer the probability of a patient having active pulmonary-TB from personal data and clinical symptoms i.e. age, gender, cough, fever, weight loss, smoker, night sweats, hospitalisation, chest pain, dyspnea, and hemoptysis.
|
98%a
|
32%a
|
US$2
|
|
T6- TPP (optimal)
|
A theoretical optimal target product profile (TPP) as proposed by Denkinger et al. [21]
|
95%
|
80%
|
US$2
|
|
T7- TPP (minimal)
|
A theoretical target product profile (TPP) with the minimum characteristics required to be useful as proposed by Denkinger et al. [21]
|
90%
|
70%
|
US$2
|
