Incremental value of T-SPOT.<i>TB</i>for diagnosis of active pulmonary tuberculosis in children in a high-burden setting: a multivariable analysis
Bibliographic record
Abstract
INTRODUCTION: Interferon γ release assays (IGRAs) are increasingly used for tuberculosis (TB) infection, but their incremental value beyond patient demographics, clinical signs and conventional tests for active disease has not been evaluated in children. METHODS: The incremental value of T-SPOT.TB was assessed in 491 smear-negative children from two hospitals in Cape Town, South Africa. Bayesian model averaging was used to select the optimal set of patient demographics and clinical signs for predicting culture-confirmed TB. The added value of T-SPOT.TB over and above patient characteristics and conventional tests was measured using statistics such as the difference in the area under the receiver operating characteristic curve (AUC), the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). RESULTS: Cough longer than 2 weeks, fever longer than 2 weeks, night sweats, malaise, history of household contact and HIV status were the most important predictors of culture-confirmed TB. Binary T-SPOT.TB results did not have incremental value when added to the baseline model with clinical predictors, chest radiography and the tuberculin skin test. The AUC difference was 3% (95% CI 0% to 7%). Using risk cut-offs of <10%, 10-30% and >30%, the NRI was 7% (95% CI -8% to 31%) but the CI included the null value. The IDI was 3% (95% CI 0% to 11%), meaning that the average predicted probability across all possible cut-offs improved marginally by 3%. CONCLUSIONS: In a high-burden setting, the T-SPOT.TB did not have added value beyond clinical data and conventional tests for diagnosis of TB disease in smear-negative children.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".