Aqueous humor penetration of gatifloxacin and moxifloxacin eyedrops given in different concentrations in a wick before cataract surgery
Bibliographic record
Abstract
PURPOSE: To determine whether the penetration into the aqueous humor of gatifloxacin (Zymar) and moxifloxacin (Vigamox) eyedrops was affected by altering their concentrations in the dilating mixture in which the wick used to dilate the pupil before cataract surgery was soaked. SETTING: Pasqua Hospital, Regina, Saskatchewan, Canada. METHODS: This prospective randomized open-label study comprised 65 women and 35 men who were divided into 2 main groups. One group received 1 mL of the antibiotic in the dilating mixture and the other, 2 mL. Each group was divided into 2 subgroups, 1 for gatifloxacin and 1 for moxifloxacin. At the beginning of surgery, 0.1 mL of aqueous humor was aspirated, frozen, and couriered to the provincial laboratory for analysis by high-performance liquid chromatography. RESULTS: In the first group, the mean concentration of gatifloxacin in the aqueous humor was 0.30 microg/mL +/- 0.21 (SD) and of moxifloxacin, 0.97 +/- 0.63 microg/mL. When the volume of the antibiotic in the dilating mixture was doubled, the mean concentration increased to 0.34 +/- 0.25 microg/mL and 1.37 +/- 0.79 microg/mL, respectively. Only the increased penetration of moxifloxacin was statistically significant. CONCLUSIONS: Moxifloxacin penetrated the aqueous humor better than gatifloxacin when given in a wick soaked in the dilating mixture before cataract surgery. Only the penetration of moxifloxacin increased significantly when the volume of the antibiotic in the dilating mixture was doubled. In both groups, only moxifloxacin reached and exceeded the minimum inhibitory concentration levels for the most common ocular pathogens causing endophthalmitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".