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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".