Fluoroquinolone Antibiotic Prophylaxis to Prevent Post-Traumatic Bacterial Infectious Endophthalmitis: Using Monte Carlo Simulation to Evaluate the Probability of Success
Bibliographic record
Abstract
Abstract Purpose: Patients with open globe injuries routinely receive fluoroquinolone (FQ) prophylaxis to prevent bacterial infectious endophthalmitis. Owing to the rarity of this infection, there is an absence of clinical trials evaluating optimal prophylactic FQ dosing. To address this knowledge gap, we conducted a Monte Carlo simulation (MCS)-based study to identify the FQ dosing option(s) that optimize pharmacokinetic–pharmacodynamic FQ target attainment against common bacterial pathogens implicated in post-traumatic bacterial infectious endophthalmitis (PTBIE). Methods: Weighted mean pharmacokinetic parameters and standard deviations for ciprofloxacin, levofloxacin, and moxifloxacin were calculated from published studies in healthy volunteers. The incidence and FQ susceptibility profiles for the most common bacteria causing PTBIE were extracted from the literature. MCS was used to determine the cumulative fraction of response (CFR) for 5 FQ dosing options to determine the probability of attaining pathogen-specific target 24-hour area under the curve to minimum inhibitory concentration ratios in the vitreous humor of the eye against the 4 most common causative bacteria seen in PTBIE. Results: Moxifloxacin 400 mg po daily (M400) achieved the highest CFR (72%). Levofloxacin dosing options achieved CFRs between 54% and 63%. Ciprofloxacin dosing options achieved CFRs between 28% and 35%. Conclusion: M400 optimized the likelihood of prophylactic success in the prevention of PTBIE, and based on the study findings, M400 is predicted to optimize the probability of success compared with ciprofloxacin and levofloxacin dosing options currently endorsed by expert opinion.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".