Genomic Epidemiology of <i>Mycobacterium abscessus</i> on the Island of Montréal Is Not Suggestive of Health Care-Associated Person-to-Person Transmission
Bibliographic record
Abstract
BACKGROUND: Mycobacterium abscessus complex (MABC), an opportunistic nontuberculous mycobacteria, can lead to poor clinical outcomes in pulmonary infections. Conflicting data exist on person-to-person transmission of MABC within and across health care facilities. To investigate further, a comprehensive retrospective study across 5 health care institutions on the Island of Montréal was undertaken. METHODS: We analyzed the genomes of 221 MABC isolates obtained from 115 individuals (2010-2018) to identify possible links. Genetic similarity, defined as ≤25 single-nucleotide polymorphisms (SNPs), was investigated through a blinded epidemiological inquiry. RESULTS: Bioinformatics analyses identified 28 sequence types, including globally observed dominant circulating clones (DCCs). Further analysis revealed 210 isolate pairs within the SNP threshold. Among these pairs, there was 1 possible laboratory contamination where isolates from different patients processed in the same laboratory differed by only 2 SNPs. There were 37 isolate pairs from patients who had provided specimens from the same hospital; however, epidemiological analysis found no evidence of health care-associated person-to-person transmission between these patients. Additionally, pangenome analysis showed higher discriminatory power than core genome analysis for examining genomic similarity. CONCLUSIONS: Genomics alone is insufficient to establish MABC transmission, particularly considering the genetic similarity and wide distribution of DCCs, although pangenome analysis has the potential to add further insight. Our findings indicate that MABC infections in Montréal are unlikely attributable to health care-associated person-to-person 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".