Carbapenem-resistant Acinetobacter baumannii (CRAB) Recovered from Burn Patients
Bibliographic record
Abstract
PURPOSE: Emergence of carbapenem-resistant Acinetobacter baumannii (CRAB) and their prolonged presence in burn units increases the risk of acquisition of CRAB. Methods. From November 2012 to September 2013, 1474 burn patients were screened for CRAB isolates through testing susceptibility to imipenem and its comparators meropenem, and doripenem. Furthermore, the in vitro activity of other antibiotics against CRAB was investigated. Results. Three patients were infected with carbapenem-susceptible A. baumannii (CSAB) and 168 were infected with CRAB. Approximately one-fifth (n=32) of CRAB isolates were obtained from patients hospitalized in Burn Intensive Care Unit (BICU). Most of CRABs were isolated from wound. The mean length of stay (LOS) in hospital prior to A. baumannii isolation was significantly higher for CRAB compared to CSAB cases (P=0.04). Amongst the independent variables, percentage of total burn surface area (TBSA) significantly increased the mortality rate using multivariate logistic regression (P=0.001, OR= 16.5; 95% CI: 4.72-57.7). The majority of tested isolates were resistant to imipenem (94.8%), and to its comparators, doripenem (97.7%), and meropenem (97.7%). The susceptibility of CRAB isolates was less than 10% to all tested antibiotics except for colistin (100%), doxycycline (61.9%), gentamicin (18.5%), and tigecycline (11.9%). Conclusion. Resistance to carbapenem reduces the number of effective antibiotics. The coordinated and intensive efforts of healthcare personnel are required to meet the challenge of dissemination of CRAB.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".