Compilation of 10 Years of MIRU-VNTR Data: Canadian National Tuberculosis Laboratory’s Experience
Bibliographic record
Abstract
Tuberculosis is a significant cause of morbidity worldwide and is a priority at the provincial and federal levels in Canada. It is known that tuberculosis transmission networks are complex and span many years as well as different jurisdictions and countries. MIRU-VNTR is a universal tuberculosis genotyping method that utilizes a 24-loci pattern and it has shown promise in identifying inter and intrajurisdictional clusters within Canada. MIRU-VNTR data collected over 10 years from the National Reference Centre for Mycobacteriology (NRCM) were analyzed in this study. Some clusters were unique to a single province/territory, while others spanned multiple provinces and/or territories in Canada. The use of a universal laboratory test can enhance contact tracing, provide geographical information on circulating genotypes, and hence, aid in tuberculosis investigation by public health. The housing of all data on one platform, technical ease of the method, easy exchange of data between jurisdictions, and strong collaboration with laboratories and surveillance units at the provincial and federal levels have the potential to identify possible outbreaks in real time.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".