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. LAM is a component of the cell wall of Mycobacterium tuberculosis, the causal agent of TB, and only one LAM-based commercial assay currently exists, the Determine TB LAM Ag (Alere Inc.). The Determine TB LAM Ag is an inexpensive lateral flow assay that detects LAM from a urine sample in 30 min. The assay is noninvasive and does not require a laboratory or technical equipment. The healthcare worker places 60 μL of a urine sample onto one end of the strip test and a positive result appears at the other end as a band within 30 min. Initially, it was hoped that …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".