Vision rehabilitation of post-Descemet membrane endothelial keratoplasty Pseudomonas keratitis utilizing losartan ophthalmic solution and scleral contact lens: a case report
Bibliographic record
Abstract
BACKGROUND: Descemet membrane endothelial keratoplasty is a preferred treatment for endothelial dysfunction, but complications, such as graft detachment and Pseudomonas keratitis, may affect results. Managing post-keratoplasty infectious keratitis poses unique challenges, with incidence varying by keratoplasty type. Diagnosis typically requires corneal scraping, and treatment strategies encompass broad-spectrum antimicrobials and surgical procedures. CASE PRESENTATION: This current case study examines the treatment of Pseudomonas keratitis in a 76-year-old Jewish male patient following Descemet membrane endothelial keratoplasty, exacerbated by freshwater exposure, in the left eye. Despite initial complications such as graft detachment, a comprehensive treatment plan, including the use of losartan 0.8 mg/mL ophthalmic solution for 7 months, followed by the fitting of a custom scleral toric contact lens, alleviated ocular surface symptoms and quality of vision (by questionnaires). The scleral contact lens provided excellent centration, and the patient's best correction visual acuity enhanced from 0.9 logMAR to 0.2 logMAR, and his near best correction visual acuity improved from Jaeger 13 to Jaeger 3.0 with +2.50 D reading spectacles worn over the scleral contact lens. Additionally, the patient's 10% low-contrast distance best correction visual acuity in the left eye enhanced from 1.3 logMAR to 0.47 (-1) logMAR in photopic conditions when using the scleral contact lens. Patient data were collected after written informed consent. CONCLUSIONS: The present report emphasizes the efficacy of personalized therapeutic strategies in the postoperative management of Descemet membrane endothelial keratoplasty patients, highlighting innovative pharmacological approaches and customized scleral contact lenses in addressing complications and enhancing patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".