Serodiagnosis of tuberculous lymphadenitis using a combination of antigens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 it