Use of antibiograms and changes in bacterial resistance patterns in the Ross Tilley Burn Centre
Bibliographic record
Abstract
Infection is a leading cause of death in burn patients and increasing antimicrobial resistance has made management difficult. Antibiograms are a useful tool to guide empiric treatment of infections, however, inappropriate prescribing may influence resistance. The objective of this study is to describe trends in antibiotic susceptibilities and use in a Canadian burn population pre- (PrA) and post-introduction (PoA) of antibiograms. We performed a retrospective review of patients admitted to an ABA-verified Burn Centre for two years pre- (2013-2014) and post-introduction (2016-2017) of institutional antibiograms receiving empiric broad-spectrum antibiotics (meropenem, piperacillin-tazobactam, and/or vancomycin). A total of 864 patients were admitted during the study period with 257 patients PrA and 239 patients PoA included. Average age, % total body surface area (%TBSA), and length of stay were similar between cohorts. Administration of empiric meropenem increased (43.2% vs. 56.8%) and piperacillin-tazobactam decreased (60.6% vs. 39.4%), which was significant (p=0.002). There was a significant decrease in the overall use of empiric antibiotics (p=0.002) and sepsis (p=0.008) since the inception of antibiograms. There was no significant difference in use of targeted antibiotics pre- or post-antibiogram introduction. Our study demonstrates that since the introduction of antibiograms, there has been a decrease in overall use of empiric antibiotics, a significant decrease in administration of piperacillin-tazobactam, and improvement in sepsis rates. However, these antibiotics were not routinely targeted to the appropriate organism and therefore may contribute to multi-drug resistant organisms in a burn population.
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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 | 0.000 |
| 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 | 0.000 |
| 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".