Impact of Targeted Testing for Latent Tuberculosis Infection Using Commercially Available Diagnostics
Bibliographic record
Abstract
BACKGROUND: The interferon-γ release assays (IGRAs) are increasingly being used as an alternative to the tuberculin skin test (TST). Although IGRAs may have better specificity and certain logistic advantages to the TST, their use may contribute to overtesting of low-prevalence populations if testing is not targeted. The objective of this study was to evaluate the accuracy of a risk factor questionnaire in predicting a positive test result for latent tuberculosis infection using the 3 commercially available diagnostics. METHODS: A cross-sectional comparison study was performed among recruits undergoing Army basic training at Fort Jackson, South Carolina, from April through June 2009. The tests performed included: (1) a risk factor questionnaire; (2) the QuantiFERON Gold In-Tube test (Cellestis Limited, Carnegie, Victoria, Australia); (3) the T-SPOT.TB test (Oxford Immunotec Limited, Abingdon, United Kingdom); and (4) the TST (Sanofi Pasteur Ltd., Toronto, Ontario, Canada). Prediction models used logistic regression to identify factors associated with positive test results. RFQ prediction models were developed independently for each test. RESULTS: Use of a 4-variable model resulted in 79% sensitivity, 92% specificity, and a c statistic of 0.871 in predicting a positive TST result. Targeted testing using these risk factors would reduce testing by >90%. Models predicting IGRA outcomes had similar specificities as the skin test but had lower sensitivities and c statistics. CONCLUSIONS: As with the TST, testing with IGRAs will result in false-positive results if the IGRAs are used in low-prevalence populations. Regardless of the test used, targeted testing is critical in reducing unnecessary testing and treatment. CLINICAL TRIAL REGISTRATION: NCT00804713.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".