Novel lipoarabinomannan point-of-care tuberculosis test for people with HIV: a diagnostic accuracy study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Most tuberculosis-related deaths in people with HIV could be prevented with earlier diagnosis and treatment. The only commercially available tuberculosis point-of-care test (Alere Determine TB LAM Ag [AlereLAM]) has suboptimal sensitivity, which restricts its use in clinical practice. The novel Fujifilm SILVAMP TB LAM (FujiLAM) assay has been developed to improve the sensitivity of AlereLAM. We assessed the diagnostic accuracy of the FujiLAM assay for the detection of tuberculosis in hospital inpatients with HIV compared with the AlereLAM assay. METHODS: For this diagnostic accuracy study, we assessed biobanked urine samples obtained from the FIND Specimen Bank and the University of Cape Town Biobank, which had been collected from hospital inpatients (aged ≥18 years) with HIV during three independent prospective cohort studies done at two South African hospitals. Urine samples were tested using FujiLAM and AlereLAM assays. The conduct and reporting of each test was done blind to other test results. The primary objective was to assess the diagnostic accuracy of FujiLAM compared with AlereLAM, against microbiological and composite reference standards (including clinical diagnoses). FINDINGS: Between April 18, 2018, and May 3, 2018, urine samples from 968 hospital inpatients with HIV were evaluated. The prevalence of microbiologically-confirmed tuberculosis was 62% and the median CD4 count was 86 cells per μL. Using the microbiological reference standard, the estimated sensitivity of FujiLAM was 70·4% (95% CI 53·0 to 83·1) compared with 42·3% (31·7 to 51·8) for AlereLAM (difference 28·1%) and the estimated specificity of FujiLAM was 90·8% (86·0 to 94·4) and 95·0% (87·7-98·8) for AlereLAM (difference -4·2%). Against the composite reference standard, the specificity of both assays was higher (95·7% [92·0 to 98·0] for FujiLAM vs 98·2% [95·7 to 99·6] for AlereLAM; difference -2·5%), but the sensitivity of both assays was lower (64·9% [50·1 to 76·7] for FujiLAM vs 38·2% [28·1 to 47·3] for AlereLAM; difference 26·7%). INTERPRETATION: In comparison to AlereLAM, FujiLAM offers superior diagnostic sensitivity, while maintaining specificity, and could transform rapid point-of-care tuberculosis diagnosis for hospital inpatients with HIV. The applicability of FujiLAM for settings of intended use requires prospective assessment. FUNDING: Global Health Innovative Technology Fund, UK Department for International Development, Dutch Ministry of Foreign Affairs, Bill & Melinda Gates Foundation, German Federal Ministry of Education and Research, Australian Department of Foreign Affairs and Trade, Wellcome Trust, Department of Science and Technology and National Research Foundation of South Africa, and South African Medical Research Council.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it