| Autor: |
Gunasekera, Kenneth S, Marcy, Olivier, Muñoz, Johanna, Lopez-Varela, Elisa, Sekadde, Moorine P, Franke, Molly F, Bonnet, Maryline, Ahmed, Shakil, Amanullah, Farhana, Anwar, Aliya, Augusto, Orvalho, Aurilio, Rafaela Baroni, Banu, Sayera, Batool, Iraj, Brands, Annemieke, Cain, Kevin P, Carratalá-Castro, Lucía, Caws, Maxine, Click, Eleanor S, Cranmer, Lisa M, García-Basteiro, Alberto L, Hesseling, Anneke C, Huynh, Julie, Kabir, Senjuti, Lecca, Leonid, Mandalakas, Anna, Mavhunga, Farai, Myint, Aye Aye, Myo, Kyaw, Nampijja, Dorah, Nicol, Mark P, Orikiriza, Patrick, Palmer, Megan, Sant'Anna, Clemax Couto, Siddiqui, Sara Ahmed, Smith, Jonathan P, Song, Rinn, Thuong Thuong, Nguyen Thuy, Ung, Vibol, van der Zalm, Marieke M, Verkuijl, Sabine, Viney, Kerri, Walters, Elisabetta G, Warren, Joshua L, Zar, Heather J, Marais, Ben J, Graham, Stephen M, Debray, Thomas P A, Cohen, Ted, Seddon, James A |
| Zdroj: |
The Lancet Child & Adolescent Health; 20230101, Issue: Preprints |
| Abstrakt: |
Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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