Diagnostic accuracy of 3 urine lipoarabinomannan tuberculosis assays in HIV-negative outpatients
Bibliographic record
Abstract
BACKGROUND.Inadequate tuberculosis (TB) diagnostics are a major hurdle in the reduction of disease burden, and accurate point-of-care tests (POCTs) are urgently needed.We assessed the diagnostic accuracy of Fujifilm SILVAMP TB lipoarabinomannan (FujiLAM) POCT for TB diagnosis in HIV-negative outpatients and compared it with Alere Determine TB LAM Ag (AlereLAM) POCT and a laboratory-based ultrasensitive electrochemiluminescence LAM research assay (EclLAM). METHODS.In this multicenter diagnostic test accuracy study, we recruited HIV-negative adults with symptoms suggestive of pulmonary TB presenting to outpatient health care centers in Peru and South Africa.Urine samples were tested using FujiLAM, AlereLAM, and EclLAM, and the diagnostic accuracy was assessed against a microbiological reference standard (MRS) and a composite reference standard. RESULTS.Three hundred seventy-two HIV-negative participants were included and the prevalence of microbiologically confirmed TB was 30%.Compared with the MRS, the sensitivities of AlereLAM, FujiLAM, and EclLAM were 10.8% (95% confidence interval [CI] 6.3%-18.0%),53.2% (95% CI 43.9%-62.1%),and 66.7% (95% CI 57.5%-74.7%),respectively.The specificities of AlereLAM, FujiLAM, and EclLAM were 92.3% (95% CI 88.5%-95.0%),98.9% (95% CI 96.7%-99.6%),and 98.1% (95% CI 95.6%-99.2%),respectively.Positive likelihood ratios of AlereLAM, FujiLAM, and EclLAM were 1.4, 46.2, and 34.8, respectively, and positive predictive values were 37.5%, 95.2%, and 93.7%, respectively. CONCLUSION.Compared with AlereLAM, FujiLAM detected 5 times more patients with TB in HIV-negative participants, had a high positive predictive value, and has the potential to improve rapid diagnosis of TB at the point-of-care.EclLAM demonstrated that additional sensitivity gains are possible, which highlights LAM's potential as a biomarker.Additional research is required to assess FujiLAM's performance in prospective cohorts, its cost-effectiveness, and its impact in real-world clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".