Within-Subject Variability of Interferon-g Assay Results for Tuberculosis and Boosting Effect of Tuberculin Skin Testing: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Variability in interferon-gamma release assays (IGRAs) results for tuberculosis has implications for interpretation of results close to the cut-point, and for defining thresholds for test conversion and reversion. However, little is known about the within-subject variability (reproducibility) of IGRAs. Several national guidelines recommend a two-step testing procedure (tuberculin skin test [TST] followed by IGRA) for the diagnosis of LTBI. However, the effect of a preceding TST on subsequent IGRA results has been reported in studies with apparently conflicting results. METHODOLOGY/FINDINGS: We conducted a systematic review to synthesize evidence on within-subject variability of IGRA results and the potential boosting effect of TST. We searched several databases and reviewed citations of previous reviews on IGRAs. We included studies using commercial IGRAs, in addition to non-commercial versions of the ELISPOT assay. Four studies, fulfilling our predefined criteria, examined within-subject variability and 13 studies evaluated TST effects on subsequent IGRA responses. Meta-analysis was not considered appropriate because of heterogeneity in study methods, assays, and populations. Although based on limited data, within-subject variability was present in all studies but the magnitude varied (16-80%) across studies. A TST induced "boosting" of IGRA responses was demonstrated in several studies and although more pronounced in IGRA-positive (i.e. sensitized) individuals, also occurred in a smaller but not insignificant proportion of IGRA-negative subjects. The TST appeared to affect IGRA responses only after 3 days and may apparently persist for several months, but evidence for this is weak. CONCLUSIONS/SIGNIFICANCE: Although reproducibility data are scarce, significant within person IGRA variability has been reported. If confirmed in more studies, this has implications for the interpretation of results close to the cut-point and for definition of conversions and reversions. Although the effect of TST on IGRA results is likely to be inconsequential in IGRA-positive subjects, in IGRA-negative subjects, the interpretation of results may be confounded by a preceding TST if administered more than 3 days prior to an IGRA.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".