Evaluation of the Impact of Interferon-Gamma Release Assays on the Management of Childhood Tuberculosis
Bibliographic record
Abstract
BACKGROUND: Interferon-gamma release assays are increasingly being used in low-incidence settings, but there is little information on whether test results influence clinical decisions in children. METHODS: In June 2009, the Montreal Children's Hospital began implementing the QuantiFERON-TB Gold In-Tube (QFT) as a follow-up test to the tuberculin skin test (TST). Pediatric respirologists were asked to document how the QFT result changed their initial clinical management based on the TST. RESULTS: During a 2-year period, 399 children with TST and QFT results were recruited prospectively. The median age was 13 years. In the cohort, 83% were foreign-born and 82% were Bacille Calmette-Guérin vaccinated. The QFT was negative in 5 of 11 (45.5%) children diagnosed with active tuberculosis (TB). Among 55 TST+/QFT- children evaluated as TB contacts, the negative QFT changed the treatment decision in only 3 (5.5%), and isoniazid was prescribed to the remainder. In 201 TST+/QFT- children from targeted school and immigrant screening programs, a negative QFT result was used to withhold isoniazid in 145 (72.1%) children. These children were followed for 1 year, during which no TB cases occurred. In a multivariable analysis, history of TB contact and TST induration ≥20 mm were associated with fewer changes in clinical decisions. CONCLUSIONS: Our cohort study showed that pediatric respirologists used negative QFT results to withhold isoniazid in most low-risk children who were referred for a positive TST found through targeted screening programs. In contrast, in almost all TST-positive children who were evaluated as TB contacts, negative QFT results did not change clinical management.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".