First independent evaluation of QuantiFERON-TB Plus performance
Bibliographic record
Abstract
Tuberculosis elimination requires an effective strategy to diagnose and treat people infected with Mycobacterium tuberculosis who would otherwise be at high risk of developing and transmitting active disease [1, 2]. The diagnostic tools for latent tuberculosis infection (LTBI) are the tuberculin skin test (TST) and the T-cell interferon-γ release assays (IGRAs). Two IGRAs are commercially available, QuantiFERON-TB Gold In-Tube (QFT-GIT) (Qiagen, Hilden, Germany) and T-SPOT.TB (Oxford Immunotec, Abingdon, UK). Compared to the TST, IGRAs offer operational advantages and higher specificity in the bacille Calmette–Guérin (BCG)-vaccinated population [3], and they are at least as sensitive for LTBI [4]. However, IGRAs have limitations: reduced sensitivity in children and immunocompromised subjects, including HIV-infected individuals [3, 4]; failure to discriminate between active tuberculosis and LTBI; and poor correlation with the risk of progression to active disease [3]. QuantiFERON-TB Plus improves sensitivity for active TB and maintains high specificity among unvaccinated controls <http://ow.ly/XjYPK> The authors thank the study subjects for their generous participation, and G. Pellicciotta, P. Erba and I. Mascherona (IRCCS San Raffaele Scientific Institute, Health Care Staff Protection Unit, Milan, Italy) for their valuable support in control enrolment.
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How this classification was reachedexpand
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".