Epidemiology of carbapenem resistant Acinetobacter baumannii in New Zealand
Bibliographic record
Abstract
AIM: Carbapenem resistant Acinetobacter baumannii have limited treatment options and a propensity to cause hospital outbreaks. In recent years an increase in their detection has been observed in New Zealand. This study aimed to describe the molecular epidemiology of these isolates. METHOD: This study utilised carbapenem resistant A. baumannii complex isolates identified across New Zealand between January 2010 to April 2018. Whole genome sequence analysis and associated demographic information was used to contextualise local isolates within the global epidemiology and establish the relationship between isolates. RESULTS: Thirty-three carbapenem resistant A. baumannii complex isolates (31 A. baumannii sensu stricto) were identified. Twenty-four (73%) were from January 2015 onwards. Twenty-four (73%) had an identifiable epidemiological link to overseas hospitalisation. Twenty-three (74%) of 31 A. baumannii sensu stricto were sequence type (ST) 2 (Pasteur scheme). Phylogenetic analysis identified three ST2 clusters. The largest cluster, of 12 isolates, was from 2015 onwards; with nine (75%) associated with recent hospitalisation in Fiji or Samoa. CONCLUSION: Increasing numbers of carbapenem resistant A. baumannii are being identified in New Zealand. Our data show that this is in large part associated with transnational spread of a single A. baumannii sensu stricto ST 2 strain between Fiji, Samoa and New Zealand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".