Serodiagnosis of tuberculous lymphadenitis using a combination of antigens
Bibliographic record
Abstract
BACKGROUND: The diagnosis of extra-pulmonary tuberculosis (EPTB) by conventional methods such as culture and microscopy has low sensitivity and requires an invasive procedure. A simple rapid serological test would be of great value. METHODS: Six antigens (ESAT-6, Ag85A, TB10.4, Rv3881c, lipoarabinomannan (LAM) and Ara6-BSA) were tested in an ELISA to detect antigen specific IgG and IgM antibodies in sera from 54 culture and histology-confirmed tuberculous lymphadenitis (TBLN) patients, among whom four were HIV seropositive, sera from 25 smear positive pulmonary tuberculosis (PTB) patients, 15 culture and histology-negative lymphadenitis (non-TBLN) patients (n=15) and 22 healthy controls (HCs). RESULTS: The sensitivities of the antigens for the detection of IgG in sera of TBLN patients ranged from 4 to 30 %. Specificities ranged from 91 to 100 % with sera from HCs. Sensitivities of the antigens for detection of IgM ranged from 0 to 15 % and specificities ranged from 91 to 100 %. LAM was the most potent antigen followed by ESAT-6 and Rv3881c for detection of IgG. However, the sensitivity for antigen specific IgG antibody detection was improved when LAM was combined with ESAT-6 and Rv3881c.The sensitivity was 54 % and the specificity 91 %. CONCLUSIONS: The study suggests that the combined use of LAM, ESAT-6 and Rv3881c for the detection of IgG in sera of TBLN patients could be a supplement to microscopy of fine- needle aspirate (FNA) to diagnose EPTB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".