An Optimal Diagnostic Strategy for Tuberculosis in Hospitalized HIV-Infected Patients Using GeneXpert MTB/RIF and Alere Determine TB LAM Ag
Bibliographic record
Abstract
analysis of 561 HIV-infected sputum-expectorating patients (median CD4 count of 130 cells/ml) from a previously published randomized controlled trial evaluating the LAM test in hospitalized HIV-infected patients with suspected TB. We evaluated 5 different diagnostic strategies using sputum culture as a reference standard (Xpert alone, LAM alone, sequential Xpert followed by LAM and vice versa [LAM in Xpert-negative patients and Xpert in LAM-negative patients], and both tests concurrently [LAM + Xpert]). A cost-consequence analysis was performed. Strategy-specific sensitivity and specificity, using culture as a reference, were similar with the Xpert-only and sequential and concurrent strategies. However, when any positive TB-specific test was used as a reference, the incremental yield of LAM over Xpert was 29.6% (45/152) and that of Xpert over LAM was 75% (84/11). The incremental yield of LAM increased with decreasing CD4 count. The costs per TB case diagnosed were similar for the sequential and concurrent strategies ($1,617 to $1,626). In sputum-expectorating hospitalized patients with advanced HIV and access to both tests, concurrent testing with Xpert and LAM may be the best strategy for diagnosing TB. These data inform clinical practice in settings where TB and HIV are endemic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".