How Well Does TSTin3D Predict Risk of Active Tuberculosis in the Canadian Immigrant Population? An External Validation Study
Bibliographic record
Abstract
BACKGROUND: The online Tuberculin Skin Test/Interferon Gamma Release Assay (TST/IGRA) Interpreter V3.0 (TSTin3D), a tool for estimating the risk of active tuberculosis (TB) in individuals with latent TB infection (LTBI), has been in use for more than a decade, but its predictive performance has never been evaluated. METHODS: People with a positive TST or IGRA result from 1985 to 2015 were identified using a health data linkage that involved migrants to British Columbia, Canada. Comorbid conditions at the time of LTBI testing were identified from physician claims, hospitalizations, vital statistics, outpatient prescriptions, and kidney and HIV databases. The risk of developing active TB within 2 and 5 years was estimated using TSTin3D. The discrimination and calibration of these estimates were evaluated. RESULTS: A total of 37 163 individuals met study inclusion criteria; 10.4% were tested by IGRA. Generally, the TSTin3D algorithm assigned higher risks to demographic and clinical groups known to have higher active TB risks. Concordance estimates ranged from 0.66 to 0.68 in 2- and 5-year time frames. Comparing predicted to observed counts suggests that TSTin3D overestimates active TB risks and that overestimation increases over time (with relative bias of 3% and 12% in 2- and 5-year periods, respectively). Calibration plots also suggest that overestimation increases toward the upper end of the risk spectrum. CONCLUSIONS: TSTin3D can discriminate adequately between people who developed and did not develop active TB in this linked database of migrants with predominately positive skin tests. Further work is needed to improve TSTin3D's calibration.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".