Influence of Ciprofloxacin Prophylaxis on the Antimicrobial Susceptibility Profile of Gram-Negative Bacilli Recovered From the Bloodstream of Patients Admitted to a Hematology/Bone Marrow Transplant Service
Bibliographic record
Abstract
Background. Fluoroquinolones are widely prescribed for febrile neutropenia prophylaxis. However, antimicrobial-resistant bacteria can be selected out during their use. The primary objective of this study was to determine the impact of ciprofloxacin (CIP) prophylaxis on the antimicrobial susceptibility profile of Gram-negative bacilli isolated from the bloodstream of patients admitted to a hematology/bone marrow transplant (BMT) service. Methods. Patients admitted to a hematology/BMT service in Winnipeg (Manitoba, Canada) between 2010 and 2015 with Gram-negative bacteremia were retrospectively identified from a review of the microbiology laboratory database. Patients could be included more than once in the analysis if at least one week had passed between bacteremia episodes. A chart review was performed for all patients. Abstracted data included patient demographic information, the isolate antimicrobial susceptibility profile, and receipt (or not) of CIP prophylaxis. Results. In total, 70 episodes of Gram-negative bacteremia occurred among 48 patients over the study period (susceptibility profile in the table below). The most common pathogens were Escherichia coli (36%), Pseudomonas aeruginosa (14%), Klebsiella pneumoniae (13%), Stenotrophomonas maltophila (9%), and Acinetobacter spp. (7%). Twenty isolates were recovered from patients receiving CIP prophylaxis. Conclusion. Gram-negative bacilli recovered from the bloodstream of patients receiving CIP prophylaxis tended to be more resistant to all antimicrobials evaluated, relative to those isolates recovered from patients not on prophylaxis. Meropenem was the most active antimicrobial, with 88.4% of isolates remaining susceptible in vitro. 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".