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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".