Diagnosing tuberculosis with urine lipoarabinomannan: systematic review and meta-analysis
Bibliographic record
Abstract
Lipoarabinomannan (LAM) is a potential marker of active tuberculosis (TB). We performed a systematic review and meta-analysis regarding use of urinary LAM assays for diagnosing active TB. We systematically searched for published and unpublished studies that evaluated urinary LAM for active TB diagnosis. Extracted data were pooled using bivariate random effects models and hierarchical summary receiver operating characteristic curves. Heterogeneity was explored through subgroup analysis and meta-regression. Quality was assessed according to standardised QUADAS (Quality Assessment of Diagnostic Accuracy Studies) criteria. In seven studies that assessed test accuracy in microbiologically confirmed cases only, estimates of sensitivity ranged from 13% to 93%, while specificity ranged from 87% to 99%. In five studies that assessed accuracy in clinical and confirmed TB cases, sensitivity ranged from 8% to 80%, while specificity ranged from 88% to 99%. In five studies with results stratified by HIV status, sensitivity was 3-53% higher in HIV-positive than HIV-negative subgroups; sensitivity was highest with advanced immunosuppression. The LAM urinary assay has several characteristics that make it attractive for diagnosing active TB, but has suboptimal sensitivity for routine clinical use. Further studies are needed to evaluate the potential value of the LAM assay in individuals with advanced HIV or for diagnosis of paediatric TB.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".