Challenges with QuantiFERON-TB Gold Assay for Large-Scale, Routine Screening of U.S. Healthcare Workers
Bibliographic record
Abstract
RATIONALE: North American occupational health programs that switched from the tuberculin skin test (TST) to IFN-γ release assays for latent tuberculosis screening are reporting challenges with interpretation of serial testing results in healthcare workers (HCWs). However, limited data exist on the reproducibility of serial IFN-γ release assay results in low-risk HCWs. OBJECTIVES: To evaluate the short-term reproducibility of QuantiFERON-TB Gold In-Tube (QFT) in a large cohort of HCWs and to define a QFT cutoff yielding a conversion rate equivalent to historical TST rates. METHODS: We retrospectively evaluated the QFT results from HCWs with two or more QFT tests performed between June 2008 and July 2010 at an academic institution. Outcome measures were proportions of reproducibility, quantitative results, and conversion rates with alternate QFT cutoffs. MEASUREMENTS AND MAIN RESULTS: A total of 9,153 HCWs with two or more QFT tests were included in the analysis. Of 8,227 individuals with a negative result, 4.4% (n = 361) converted their QFT result over 2 years. A total of 261 (72.3%) of the HCWs with conversions underwent repeat short-term testing after the first positive result with 64.8% reverting (n = 169). An IFN-γ cutoff of 5.3 IU/ml or higher (manufacturer's cutoff is ≥0.35 IU/ml) yielded a conversion rate of 0.4%, equal to our institution's historical TST conversion rate. CONCLUSIONS: The manufacturer's definition of QFT conversion results in an inflated conversion rate that is incompatible with our low-risk setting. A significantly higher QFT cutoff value is needed to match the historical TST conversion rate. Nonreproducible conversions in most converters suggested false-positive 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 | 0.074 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".