Impact of Blood Volume, Tube Shaking, and Incubation Time on Reproducibility of QuantiFERON-TB Gold In-Tube Assay
Bibliographic record
Abstract
Gamma interferon (IFN-γ) release assays (IGRAs) are functional assays used serially to measure the efficacy of novel tuberculosis (TB) vaccines and to screen health care workers for latent tuberculosis infection (LTBI). However, studies have shown nonreproducible IGRA results. In this study, we investigated the effects of blood volume (0.8, 1.0, and 1.2 ml), tube shaking (gentle versus vigorous), and incubation duration (16, 20, and 24 h) on the reproducibility of QuantiFERON-TB Gold In-Tube (QFT-GIT) results for 50 subjects (33 uninfected and 17 infected). The median IFN-γ TB response (TB antigen [Ag] minus nil value) was significantly higher with 0.8 ml blood (1.04 IU/ml) than with 1.0 ml (0.85 IU/ml; P = 0.002) or 1.2 ml (0.49 IU/ml; P < 0.001) for subjects with LTBI. Compared with 0.8 ml (11.8%), there were larger proportions of false-negative results with 1.0 ml (29.4%; P = 0.2) and 1.2 ml (41.2%; P = 0.05) of blood for infected subjects. Blood volume did not significantly change the proportions of positive results in uninfected controls. Compared with gentle shaking, vigorous shaking increased the median IFN-γ response in nil (0.04 versus 0.06 IU/ml; P < 0.001) and TB Ag (0.12 versus 0.24 IU/ml; P = 0.004) tubes and increased TB responses (TB Agvigorous minus nilgentle) (0.02 versus 0.08 IU/ml; P = 0.004). The duration of incubation did not have a significant impact on the proportion of positive results in uninfected or infected subjects. This study identified blood volume and tube shaking as novel preanalytical sources of variability which require further standardization in order to improve the quality and reproducibility of QFT-GIT results.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".