Diagnostic accuracy of induced sputum LAM ELISA for tuberculosis diagnosis in sputum-scarce patients
Bibliographic record
Abstract
OBJECTIVE: To examine whether a lipoarabinomannan (LAM) enzyme-linked immunosorbent assay (ELISA) that offers diagnostic utility using urine in patients with tuberculosis (TB) and human immunodeficiency virus (HIV) co-infection can be used in induced sputum to diagnose sputum-scarce patients with suspected TB. DESIGN: LAM was measured in induced sputum samples obtained from 61 consecutively recruited sputum-scarce TB suspects in a tertiary hospital respiratory clinic in South Africa. Liquid culture positivity for Mycobacterium tuberculosis was used as the reference standard. Receiver operating characteristic analysis was used to assess alternative LAM concentration cut-offs. RESULTS: A total of 87% (53/61) of study patients had a valid M. tuberculosis culture result; 49% (23/53) were HIV-infected and 17% (9/53) were culture-positive for M. tuberculosis. Induced sputum smear microscopy and LAM ELISA had an overall sensitivity of 56% (95%CI 27-81); however, the specificity of LAM ELISA was 48% (95%CI 34-62), while the positive and negative predictive values were respectively 18% (95%CI 8-36) and 84% (95%CI 65-94). An optimal rule-in cut-off selected by receiver operating characteristic (LAM concentration >5.73 ng/ml) increased test specificity to 98% and reduced sensitivity to 22%. Normalisation of the assay for sample total protein or cell count did not improve diagnostic accuracy. CONCLUSIONS: In this proof-of-concept study, the ELISA was not clinically useful for TB diagnosis using induced sputum.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
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".