Urine Lipoarabinomannan for Tuberculosis Diagnosis: Evolution and Prospects
Bibliographic record
Abstract
Tuberculosis (TB)4 affects over 10 million people and kills 1.7 million each year. This devastating epidemic is unacceptable, as, given correct diagnosis and timely initiation of treatment, TB is curable. Unfortunately, case detection is the weakest step in the cascade of care, and about 40% of TB patients are either not diagnosed or not reported to the health system. This is partly attributable to limitations of existing diagnostics, which are either inaccurate or inaccessible, particularly at the primary care level, where most patients begin seeking care with nonspecific symptoms such as cough and fever. A simple diagnostic test that can be performed at the point of care (POC) in primary care settings has been near the top of the TB community's wish list for many years, and the WHO has published target product profiles (TPPs) with detailed specifications for such a test (1). While no existing TB test meets all POC TB test TPP requirements, the rapid urine lipoarabinomannan (LAM) detection test comes close because of its simplicity, low cost, and ability to be used in decentralized 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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".