Gram-Negative Bacteria That Produce Carbapenemases Causing Death Attributed to Recent Foreign Hospitalization
Bibliographic record
Abstract
Overseas travel, as a risk factor for the acquisition of infections due to antimicrobial-resistant organisms, has recently been linked to carbapenemase-producing Gram-negative bacteria. Multiresistant Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii strains were isolated from a wound of a Canadian patient with a recent history of hospitalization in India. This resulted in the initiation of outbreak management that included surveillance cultures. Epidemiological and molecular investigations showed that NDM-1-producing K. pneumoniae ST16 and OXA-23-producing A. baumannii ST10 strains were transmitted to 5 other patients, resulting in the colonization of 4 patients and the death of 1 patient due to septic shock caused by the OXA-23-producing A. baumannii strain. The high rate of false positivity of the screening cultures resulted in additional workloads and increased costs for infection control and clinical laboratory work. We believe that this is the first report of an infection with carbapenemase-producing Gram-negative bacteria resulting in death attributed to a patient with recent foreign hospitalization. We recommend routine rectal and wound screening for colonization with multiresistant bacteria for patients who have recently been admitted to hospitals outside Canada.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".