Diagnosing active tuberculosis in people living with HIV
Bibliographic record
Abstract
PURPOSE OF REVIEW: Diagnosis of tuberculosis (TB) in people living with HIV (PLHIV) remains a challenge, despite it being the most common comorbidity in this group. In this review, we provide an overview of tests for active TB, and their diagnostic performance in PLHIV. RECENT FINDINGS: New and updated diagnostic tests have better performance than traditional bacterial culture or smear microscopy in PLHIV. Recent developments in molecular tests have improved the sensitivity at which TB and drug susceptibility can be detected in PLHIV. Notably, the updated Xpert Ultra test can detect HIV-associated TB with high sensitivity, and a rapid lateral flow lipoarabinomannan-based assay has been shown to reduce TB-related mortality in hospitalized PLHIV. New directions in drug-susceptibility testing are being pursued, such as next-generation sequencing and line probe assays, but more evaluation in PLHIV is needed. There is growing understanding of subclinical TB, but methods to detect this type of TB are inadequate. SUMMARY: As diagnosis is the weakest link in the TB care cascade, newer more accurate TB tests must be scaled up and fully integrated into existing healthcare systems. Drug-susceptibility testing must become universal to ensure appropriate treatment regimens are prescribed, allowing TB clearance and inhibiting development of antimicrobial resistance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".