An Outbreak Due to Multiresistant<i>Acinetobacter baumannii</i>in a Burn Unit: Risk Factors for Acquisition and Management
Bibliographic record
Abstract
OBJECTIVES: To describe the investigation and management of an outbreak due to multiresistant Acinetobacter baumannii and to determine risk factors for acquisition of the organism. SETTING: A 14-bed regional burn unit in a Canadian tertiary-care teaching hospital. DESIGN: Case-control study with multivariate analysis of potential risk factors using logistic regression analysis. Surveillance cultures were obtained from the hospital environment, from noninfected patients, and from healthcare providers. RESULTS: A total of 31 (13%) of 247 patients with acute burn injuries acquired multiresistant A. baumannii between December 1998 and March 2000; 18 (58%) of the patients were infected. The organism was recovered from the hospital environment and the hands of healthcare providers. Significant risk factors for acquisition of multiresistant A. baumannii were receipt of blood products (odds ratio [OR], 10.8; 95% confidence interval [CI95], 3.4 to 34.4; P < .001); procedures performed in the hydrotherapy room (OR, 4.1; CI95, 1.3 to 13.1; P = .02); and increased duration of mechanical ventilation (OR, 1.1 per day; CI95, 1.0 to 1.1; P= .02). INTERVENTIONS: Improved compliance with hand hygiene, strict patient isolation, meticulous environmental cleaning, and temporary closure of the unit to new admissions. CONCLUSIONS: Acquisition of multiresistant A. baumannii was likely multifactorial, related to environmental contamination and contact with transiently colonized healthcare providers. Control measures addressing these potential sources of multiresistant A. baumannii were successful in terminating the outbreak. Ongoing surveillance and continued attention to hand hygiene and adequate environmental cleaning are essential to prevent recurrent outbreaks due to antibiotic-resistant bacteria in burn units.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".