Investigation of the Clinical Efficacy and Dosage of Intravenous Ciprofloxacin in Patients with Respiratory Infection
Bibliographic record
Abstract
PURPOSE: Infection control is particularly vital in hospitals, and proper use of antimicrobial drugs is one of the most important roles of hospital pharmacists. In this study, we surveyed patients who had been prescribed single-use ciprofloxacin (CPFX), and evaluated the blood concentration of CPFX from the predictive AUC (area under the concentration curve). METHODS: This study was performed retrospectively to 112 adult patients diagnosed as having respiratory infections who had been treated as inpatients with intravenous CPFX for more than 3 days at Toho University Omori Hospital in Tokyo. The predictive AUC of each patient was obtained from the modified formulae reported by Forrest et al. (1993) [1]. The relation between the antimicrobial activity of CPFX and pharmacokinetic/pharmacodynamic (Cmax, AUC and AUC/MIC (minimum inhibitory concentration)) was studied. RESULTS: Although CPFX is excreted from the kidney, standard treatment with this drug does not take renal function into consideration. Our results indicated that CPFX was effective in less than 50% of the patients who received it. Moreover, the AUC/MIC ratio in both the effective group and the failure group was less than 125 when the clinical target was gram-negative bacteria. CONCLUSION: These results suggest that the clinical use of CPFX for the treatment of infectious diseases does not reach the target AUC/MIC ratio, and that the concentration of CPFX is not within the range to which many pathogens are susceptible in a large proportion of patients. To ensure the effective treatment of patients with infectious diseases and to prevent the development of resistance in bacteria, we recommend therapeutic drug monitoring (TDM) of CPFX in hospitals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".