Sensitivity and specificity of spoligotyping and MIRU-VNTR typing in tuberculosis molecular epidemiology
Bibliographic record
Abstract
The standard genotyping technique in tuberculosis molecular epidemiology is IS6110 Restriction Fragment Length Polymorphism (RFLP). Several public health organizations intend to switch from IS6110 RFLP to spoligotyping and Mycobacterium Interspersed Repetitive Units - Variable Number Tandem Repeats (MIRU-VNTR); however, the relevant test characteristics have not been studied. Objective. Estimate the sensitivities and specificities of spoligotyping and MIRU-VNTR using IS6110 RFLP as the reference standard. Population. Residents of the island of Montreal diagnosed with active tuberculosis between 1996-1998, who were culture positive for Mycobacterium tuberculosis and had ≥6 copies of IS6110 upon RFLP. Outcomes. Sensitivity, specificity, percent transmission. Estimated sensitivities. spoligotyping 83% (95% CI 63%-95%), MIRU-VNTR 52% (31%-72%), MIRU-VNTR and spoligotyping used in combination (clustered = both identical) 50% (29%-71%). Estimated specificities. spoligotyping 40% (35%-46%), MIRU-VNTR 56% (51%-62%), combination 70% (65%-76%). Percent transmission increased from 4% (IS6110 identical RFLP) to 23% (combination), 33% (MIRU-VNTR), and 53% (spoligotyping). There was evidence of misclassification. The poor test characteristics of spoligotyping and MIRU-VNTR typing suggest that these methods are not suitable 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 | 0.018 | 0.050 |
| 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) | 0.001 | 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".