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Record W2075803850 · doi:10.1159/000196353

Value of ELISA Using A60 Antigen in the Serodiagnosis of Tuberculosis

2009· article· en· W2075803850 on OpenAlexfundno aff
José A. Caminero, Felipe Rodrı́guez de Castro, Teresa Carrillo, B Lafarga, F. Díaz, Pedro Cabrera

Bibliographic record

VenueRespiration · 2009
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersIndigenous and Northern Affairs Canada
KeywordsMedicineTuberculosisSputumMycobacterium tuberculosisExtrapulmonary tuberculosisInternal medicineAntibodyAntigenSputum cultureGastroenterologyImmunologyPathology

Abstract

fetched live from OpenAlex

This study was set in a general, university hospital in the Canary Islands, with the objective to evaluate an enzyme-linked immunosorbent assay (ELISA) using A60 in the serodiagnosis of tuberculosis. IgG antibody to A60 was determined in 205 patients with active disease [149 culture-positive patients with pulmonary tuberculosis (positive sputum smear 94, negative sputum smear 55) and 56 patients with extrapulmonary tuberculosis], 20 patients with inactive disease, 22 patients with lepromatous leprosy, and 51 controls. The mean levels of anti-A60 antibodies were significantly higher in patients with active disease as compared with controls or patients with inactive disease. Differences were also found between tuberculous patients with pulmonary and extrapulmonary disease. In patients with pulmonary disease, significant differences were detected between smear-positive and smear-negative patients. The overall sensitivity of the test (cutoff 240 ELISA units) was 52.2%. The highest sensitivity was found among smear-positive patients with pulmonary tuberculosis (67%) and the lowest among those with extrapulmonary tuberculosis (32.1%). We conclude that ELISA for the measurement of IgG antibody to Mycobacterium tuberculosis antigen A60 could be of interest, specially in smear-negative cases and extrapulmonary tuberculosis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.381
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2009
Admission routes1
Has abstractyes

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