Whole genome sequencing for improved understanding of <i>Mycobacterium tuberculosis</i> transmission in a remote circumpolar region
Bibliographic record
Abstract
Few studies have used genomic epidemiology to understand tuberculosis (TB) transmission in rural and remote settings - regions often unique in history, geography and demographics. To improve our understanding of TB transmission dynamics in Yukon Territory (YT), a circumpolar Canadian territory, we conducted a retrospective analysis in which we combined epidemiological data collected through routine contact investigations with clinical and laboratory results. Mycobacterium tuberculosis isolates from all culture-confirmed TB cases in YT (2005-2014) were genotyped using 24-locus Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats (MIRU-VNTR) and compared to each other and to those from the neighbouring province of British Columbia (BC). Whole genome sequencing (WGS) of genotypically clustered isolates revealed three sustained transmission networks within YT, two of which also involved BC isolates. While each network had distinct characteristics, all had at least one individual acting as the probable source of three or more culture-positive cases. Overall, WGS revealed that TB transmission dynamics in YT are distinct from patterns of spread in other, more remote Northern Canadian regions, and that the combination of WGS and epidemiological data can provide actionable information to local public health teams.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".