Measurement of antimicrobial resistance in the respiratory microbiota and antimicrobial use in nine intensive care units, using different definitions and indicators
Bibliographic record
Abstract
BACKGROUND: Using different indicators and definitions, the present study aimed to describe population antimicrobial use, as well as prevalence and incidence of clinically relevant antimicrobial resistances found in respiratory cultures performed in intensive care unit (ICU) patients. Results obtained with the various methodologies were then compared. METHODS: The present retrospective cohort study included all patients admitted to nine ICUs between April 2006 and March 2010. Prevalence and incidence of clinically relevant resistances in respiratory cultures were described and population antimicrobial use was measured using 10 different indicators based on dosage, duration of treatment, or exposure to antimicrobials. RESULTS: Indicators had variable sensitivity to detect time trends and differences among ICU types. However, the highest prevalence and incidence rates in respiratory isolates were in Staphylococcus aureus resistance to oxacillin (0.52% of ICU admissions and 6.57 acquisitions/10,000 patient-days) and coliforms resistance to piperacillin-tazobactam (0.44% and 7.80 acquisitions/10,000 patient-days). Cephalosporins, penicillins, and aminoglycosides were the most frequently prescribed antimicrobials, according to most indicators. CONCLUSIONS: Given the observed heterogeneity among indicators, one should consider referring to sets of indicators, allowing for the selection of indicators representing different aspects of antimicrobial use, resistance levels, and of patient case mix.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".