Topical Losartan for Corneal Fibrosis: A Case Series With Densitometry Analysis
Bibliographic record
Abstract
PURPOSE: To present the clinical, topographic, and densitometry outcomes of patients with corneal fibrosis treated with topical losartan. METHODS: In this case series, patients with corneal scars treated with topical losartan 0.8 mg/mL 4 times a day for 6 months were included. Age, sex, cause of corneal opacity, months with corneal opacity, and previous topical treatment were recorded. Patients were examined at baseline and 1, 3, and 6 months after starting treatment. At each visit, uncorrected and best-corrected visual acuity, subjective refraction, intraocular pressure, slit-lamp examination, corneal tomography, and densitometry were performed. Patients were asked about drop comfort and possible side effects on a 0 to 10 self-reported scale. RESULTS: Eight eyes of 7 patients (4 males, 3 females, mean age 45.1 ± 12.0 years) were included. Best-corrected visual acuity logMAR was 0.28 ± 0.17 pretreatment and 0.17 ± 0.11 after 6 months of topical losartan ( P = 0.358). The visual acuity of 5 eyes improved, 1 eye remained unchanged, and the vision of 2 eyes declined. No changes in topographic and densitometry parameters were noted within the cohort analyzed as a group (all P > 0.05). No systemic side effects were reported, and tolerance was from very good to excellent (all 2/10 or better). CONCLUSIONS: No significant improvements in visual acuity and densitometry values were noted with topical losartan in this series analyzed as a group. Further research to assess the full scope of clinical applications in corneal fibrosis is needed, particularly randomized clinical trials to address the effect of time and unequivocally prove its beneficial effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".