221Antimicrobial Use in Nine Intensive Care Units, Using Ten Different Indicators
Bibliographic record
Abstract
Background. Surveillance and control of hospital antimicrobial (AM) are intended to limit AM resistance. Using 10 different indicators for AM monitoring, we aimed to measure AM use in nine intensive care units in Montréal. Methods. AM prescriptions for all patients admitted to participating ICUs (3 neonatal, 2 pediatric, 4 adult) between April 2006 and March 2010 were measured retrospectively using 10 different indicators of AM use. These indicators were obtained by combining 5 numerators [defined daily doses (DDDs), recommended daily doses (RDDs), agent-days, exposed or not, and number of courses] with 2 denominators (patient-days and admissions). Indicators were computed for by class of AM, in accordance with the Anatomical Therapeutic Chemical Classification System, and were stratified per year and ICU type. Poisson regression was used to estimate time trends and differences in AM use by type of ICU. Results. Overall, ranking of AM use by class was similar, regardless of the indicator used. When RDDs, exposed and courses were used, the most frequently used AM classes were cephalosporins, followed by penicillins and aminoglycosides. Using agent-days, penicillins came first, followed by aminoglycosides and penicillins and B-lactams inhibitors. With DDDs, more variations were observed. From 2006 to 2010, use decreased significantly for all AM classes except 1) carbapenems use, which remained stable, and 2) trimethoprim and sulfamides use, for which results varied with the indicator used. Compared to adult ICUs: 1) aminoglycosides and penicillins use was higher in both neonatal and pediatric ICUs; 2) carbapenems, glycopeptides and quinolones use was lower; 3) for cephalosporins, clindamycin, macrolides, penicillins and B-lactams inhibitors and trimethoprim and sulfamides, use was lower in neonatal ICUs and higher in pediatric ICUs. Conclusion. Frequency of AM prescribing varied across ICU types, but generally decreased in participating ICUs. A standard set of indicators would facilitate surveillance of AM use in a population. Disclosures. All authors: No reported disclosures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".