Challenges in the Development of an Immunochromatographic Interferon-Gamma Test for Diagnosis of Pleural Tuberculosis
Bibliographic record
Abstract
Existing diagnostic tests for pleural tuberculosis (TB) have inadequate accuracy and/or turnaround time. Interferon-gamma (IFNg) has been identified in many studies as a biomarker for pleural TB. Our objective was to develop a lateral flow, immunochromatographic test (ICT) based on this biomarker and to evaluate the test in a clinical cohort. Because IFNg is commonly present in non-TB pleural effusions in low amounts, a diagnostic IFNg-threshold was first defined with an enzyme-linked immunosorbent assay (ELISA) for IFNg in samples from 38 patients with a confirmed clinical diagnosis (cut-off of 300 pg/ml; 94% sensitivity and 93% specificity). The ICT was then designed; however, its achievable limit of detection (5000 pg/ml) was over 10-fold higher than that of the ELISA. After several iterations in development, the prototype ICT assay for IFNg had a sensitivity of 69% (95% confidence interval (CI): 50-83) and a specificity of 94% (95% CI: 81-99%) compared to ELISA on frozen samples. Evaluation of the prototype in a prospective clinical cohort (72 patients) on fresh pleural fluid samples, in comparison to a composite reference standard (including histopathological and microbiologic test results), showed that the prototype had 65% sensitivity (95% CI: 44-83) and 89% specificity (95% CI: 74-97). Discordant results were observed in 15% of samples if testing was repeated after one freezing and thawing step. Inter-rater variability was limited (3%; 1 out of 32). In conclusion, despite an iterative development and optimization process, the performance of the IFNg ICT remained lower than what could be expected from the published literature on IFNg as a biomarker in pleural fluid. Further improvements in the limit of detection of an ICT for IFNg, and possibly combination of IFNg with other biomarkers such as adenosine deaminase, are necessary for such a test to be of value in the evaluation of pleural tuberculosis.
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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 | 0.055 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".