Solid organ transplant–specific antibiogram in a tertiary pediatric hospital in Canada
Bibliographic record
Abstract
SOT recipients are more vulnerable to infections with antimicrobial-resistant organisms, and therefore, it may be useful for transplant centers to create transplant-specific antibiograms to direct empirical antimicrobial regimens and monitor trends in antimicrobial resistance. SOT-specific antibiograms were created using antimicrobial susceptibility data on isolates from 2012 to 2018 at The Hospital for Sick Children, Toronto, Ontario, Canada. The CLSI guidelines were followed to generate the antibiograms except that results from 2 years of data were pooled on a rolling basis to achieve larger sample sizes. The 3 most frequent organisms in one analysis period of the SOT antibiogram were Escherichia coli (average sample size ±standard deviation; n = 28.7 ± 3.8), Staphylococcus aureus (n = 27.8 ± 5.0), and Pseudomonas aeruginosa (non-CF) (n = 19.8 ± 8.8). For E.coli, susceptibilities in the SOT antibiogram were significantly lower than those in the hospital-wide antibiogram in 2017-2018 for ampicillin (27% vs 47%; p = .014), piperacillin/tazobactam (55% vs 88%; p < .001), cefotaxime (59% vs 89%; p < .001), ciprofloxacin (71% vs 88%; p = .007), and trimethoprim-sulfamethoxazole (41% vs 69%; p = .001), but not significantly different for aminoglycosides and meropenem. In the SOT antibiogram of E. coli, decreased susceptibility trend was confirmed in some antibiotics, including piperacillin/tazobactam (83% in 2012-2013 vs 55% in 2017-2018). At our center, the solid organ transplant-specific antibiogram revealed important differences in E. coli susceptibilities and trends in antimicrobial resistance. Developing a SOT antibiogram will assist in revising and improving empiric treatment guidelines as well as monitoring antimicrobial resistance in this 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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".