Comparison of Ophthalmic Gatifloxacin 0.3% and Ciprofloxacin 0.3% in Healing of Corneal Ulcers Associated with <i>Pseudomonas aeruginosa</i> –Induced Ulcerative Keratitis in Rabbits
Bibliographic record
Abstract
OBJECTIVE: Evaluation of gatifloxacin 0.3% ophthalmic solution efficacy in a corneal ulcer model of Pseudomonas keratitis. METHODS: Heptanol-induced corneal ulcers in New Zealand White rabbits (n = 41; 8 females/group) were inoculated with 10(6) CFU of Pseudomonas aeruginosa. Gatifloxacin 0.3% dosing varied among 4 groups with frequencies of 16-48 doses/day (days 1-2), 3-16 doses/day (days 3-7), and maintenance dosing of 3-4 doses/day (days 8-22). Ciprofloxacin 0.3% was administered as labeled for corneal ulcers, with 44 doses on day 1, 16 doses on day 2, and 4 doses/day on days 3-21. RESULTS: All eyes showed evidence of infection by 48 hours postinoculation with 36 of 41 eyes (87.8%) exhibiting moderate-to-severe keratitis. All eyes exhibited corneal healing by day 15, with no significant differences among groups. Three of 4 groups receiving gatifloxacin tended to have smaller fluorescein retention area scores than did the ciprofloxacin group. No eyes tested positive for Pseudomonas at the end of the study. No corneal precipitates were found following as many as 48 doses/day of gatifloxacin. CONCLUSION: Ophthalmic gatifloxacin 0.3% is at least as effective as ciprofloxacin at healing corneal ulcers infected with Pseudomonas aeruginosa when gatifloxacin is administered less frequently than ciprofloxacin. Trends favored gatifloxacin in fluorescein retention scores.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".