Enrofloxacin-associated ocular disease in cats: A scoping review.
Bibliographic record
Abstract
Background: Ocular effects, most notably retinal degeneration, have been linked to enrofloxacin use in cats. However, data have been limited and there is a need for formal evidence synthesis to better understand and characterize these potentially life-altering adverse events. Objective: The objective was to describe data regarding ocular adverse events associated with enrofloxacin administration in cats and to identify information gaps. Procedure: the Ovid platform), Web of Science, and CAB Abstracts bibliographic databases. Results: = 2 cases), and 1 pharmacovigilance summary publication. There were reports of enrofloxacin-associated adverse ocular events in 163 cats. Loss of vision, mydriasis, and altered pupillary light responses were most commonly reported. Increased tapetal reflectivity, retinal vessel attenuation, and retinal degeneration were the most common abnormalities on ophthalmological examination. Most cats had permanent blindness or altered vision. Most cats had received doses well in excess of the current label recommendation, but 15/103 (14%) cats for which dosing data were available were reported to have received ≤ 5 mg/kg per day. Conclusion and clinical relevance: , age, dose). Although a dose of ≤ 5 mg/kg per day will likely reduce the likelihood of adverse events, these data indicated that ocular disease was still possible at that dosage.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".