Genomic epidemiology of Mycobacterium abscessus in a Canadian cystic fibrosis centre
Bibliographic record
Abstract
The Mycobacterium abscessus complex causes significant morbidity and mortality among patients with Cystic Fibrosis (CF). It has been hypothesized that these organisms are transmitted from patient to patient based on genomics. However, few studies incorporate epidemiologic data to confirm this hypothesis. We longitudinally sampled 27 CF and 7 non-CF patients attending a metropolitan hospital in Ontario, Canada from 2013 to 2018. Whole genome sequencing along with epidemiological data was used to evaluate the likelihood of transmission. Overall, the genetic diversity of M. abscessus was large, with a median pairwise distance (IQR) of 1,279 (143-134) SNVs between all Ontario M. abscessus isolates and 2,908 (21-3,204) single nucleotide variants (SNVs) between M. massiliense isolates. This reflects the global diversity of this pathogen, with Ontario isolates widely dispersed throughout global phylogenetic trees of each subspecies. Using a maximum distance of 25 SNVs as a threshold to identify possible transmission, we identified 23 (of 276 total) pairs of closely-related isolates. However, transmission was probable for only one pair based on both genomic and epidemiological data. This suggests that person-to-person transmission of M. abscessus among CF patients is indeed rare and reinforces the critical importance of epidemiological data for inferences of transmission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".