Sensitivities and Specificities of Spoligotyping and Mycobacterial Interspersed Repetitive Unit-Variable-Number Tandem Repeat Typing Methods for Studying Molecular Epidemiology of Tuberculosis
Bibliographic record
Abstract
The development of PCR-based genotyping modalities (spoligotyping and mycobacterial interspersed repetitive unit-variable-number tandem repeat [MIRU-VNTR] typing) offers promise for real-time molecular epidemiological studies of tuberculosis (TB). However, the utility of these methods depends on their capacity to appropriately classify isolates. To determine the operating parameters of spoligotyping and MIRU-VNTR typing, we have compared results generated by these newer tests to the standard typing method, IS6110 restriction fragment length polymorphism, in analyses restricted to high-copy-number IS6110 isolates. Sensitivities of the newer tests were estimated as the percentages of isolates with identical IS6110 fingerprints that had identical spoligotypes and MIRU-VNTR types. The specificities of these tests were estimated as the percentages of isolates with unique IS6110 fingerprints that had unique spoligotypes and MIRU-VNTR types. The sensitivity of MIRU-VNTR typing was 52% (95% confidence interval [CI], 31 to 72%), and the sensitivity of spoligotyping was 83% (95% CI, 63 to 95%). The specificity of MIRU-VNTR typing was 56% (95% CI, 51 to 62%), and the specificity of spoligotyping was 40% (95% CI, 35 to 46%). The proportion of isolates estimated to be due to recent transmission was 4% by identical IS6110 patterns, 19% by near-identical IS6110 patterns, 33% by MIRU-VNTR typing, and 53% by spoligotyping. The low calculated specificities of spoligotyping and MIRU-VNTR typing led to misclassification of cases, inflated estimates of TB transmission, and low positive predictive values, suggesting that these techniques have unsuitable operating parameters for population-based molecular epidemiology studies.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".