How Close Is Close Enough? Exploring Matching Criteria in the Estimation of Recent Transmission of Tuberculosis
Bibliographic record
Abstract
If Mycobacterium tuberculosis isolates from 2 people have the same genotype, transmission may have occurred between them. Genotyping based on the insertion sequence IS6110 uses identical restriction fragment length polymorphisms ("fingerprints") to infer transmission. However, once transmission has occurred, the genotypes may mutate, resulting in divergent fingerprints. Estimation of the proportion of tuberculosis (TB) cases due to recent transmission includes 3 approaches to determine if genotypes match: exact matching (assumes no fingerprint change); band-addition, band-loss, band-shift matching (ad hoc attempt to account for fingerprint changes); and genetic distance (directly accounts for fingerprint changes). Via simulation study, the authors varied the fingerprint change rate, level of recent transmission, and background genetic heterogeneity and estimated sensitivity, specificity, and bias of the recent transmission index by matching method. For exact matching, specificity was always high, but sensitivity decreased as the change rate increased. For band-addition, band-loss, band-shift matching, specificity decreased as genetic diversity decreased, and sensitivity remained high as the change rate increased. Genetic distance offered a compromise between the 2. Results from this study suggest that interpretation of the recent transmission index and the resulting necessary public health interventions will vary according to how researchers account for spontaneous mutation when estimating transmission from genotyping data.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".