Nosocomial infections and antimicrobial resistance in critical care medicine
Bibliographic record
Abstract
Abstract Objective: To review the human and companion animal veterinary literature on nosocomial infections and antimicrobial drug resistance as they pertain to the critically ill patient. Data sources: Data from human and veterinary sources were reviewed using PubMed and CAB. Human data synthesis: There is a large amount of published data on nosocomially‐acquired bloodstream infections, pneumonia, urinary tract infections and surgical site infections, and strategies to minimize the frequency of these infections, in human medicine. Nosocomial infections caused by multi‐drug‐resistant (MDR) pathogens are a leading cause of increased patient morbidity and mortality, medical treatment costs, and prolonged hospital stay. Epidemiology and risk factor analyses have shown that the major risk factor for the development of antimicrobial resistance in critically ill human patients is heavy antibiotic usage. Veterinary data synthesis: There is a paucity of information on the development of antimicrobial drug resistance and nosocomially‐acquired infections in critically ill small animal veterinary patients. Mechanisms of antimicrobial drug resistance are universal, although the selection effects created by antibiotic usage may be less significant in veterinary patients. Future studies on the development of antimicrobial drug resistance in critically ill animals may benefit from research that has been conducted in humans. Conclusions: Antimicrobial use in critically ill patients selects for antimicrobial drug resistance and MDR nosocomial pathogens. The choice of antimicrobials should be prudent and based on regular surveillance studies and accurate microbiological diagnostics. Antimicrobial drug resistance is becoming an increasing problem in veterinary medicine, particularly in the critical care setting, and institution‐specific strategies should be developed to prevent the emergence of MDR infections. The collation of data from tertiary‐care veterinary hospitals may identify trends in antimicrobial drug resistance patterns in nosocomial pathogens and aid in formulating guidelines for antimicrobial use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".