Support for higher ciprofloxacin AUC24/MIC targets in treating Enterobacteriaceae bloodstream infection
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: Given concerns regarding optimal therapy for serious Gram-negative infections, the goal was to characterize the pharmacodynamics of ciprofloxacin in the context of treating bloodstream infection. PATIENTS AND METHODS: Data were collected from the medical records of 178 clinical cases. Blood isolates were retrieved and ciprofloxacin MICs were measured. Forty-two cases in which ciprofloxacin was initiated within 24 h of the positive blood culture were used in the pharmacodynamic analysis. RESULTS: Significant factors with regard to treatment failure were low ciprofloxacin AUC(24)/MIC (P < 0.0001), high MIC (P = 0.001), male sex (P = 0.002) and low AUC(24) (P = 0.01). AUC(24)/MIC (P = 0.012) and MIC (P = 0.019) were significant variables in multivariate analyses; however, only the former remained significant (P = 0.038) after excluding two cases with ciprofloxacin-resistant isolates. An AUC(24)/MIC breakpoint of 250 was most significant, with cure rates of 91.4% (32/35) and 28.6% (2/7) in patients with values above and below this threshold, respectively (P = 0.001). The risk of ciprofloxacin treatment failure was 27.8 times (95% confidence interval, 2.1-333) greater in those not achieving an AUC(24)/MIC >or=250 (P = 0.011). Monte Carlo simulation of 5000 study subjects predicted that 0.88 of the population would achieve an AUC(24)/MIC >or=250 with standard-dose ciprofloxacin (400 mg intravenously every 12 h). CONCLUSIONS: This study confirms the pharmacodynamic parameters of ciprofloxacin that are important for optimizing the treatment of serious infections, particularly the benefits of achieving an AUC(24)/MIC >or=250, rather than the conventional target of >or=125. It also shows the relevance of dose selection in optimizing target attainment, with important differences among pathogens, even those with MICs within the susceptible range.
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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 it