Anterior infectious necrotizing scleritis secondary to Pseudomonas aeruginosa infection following intravitreal ranibizumab injection
Bibliographic record
Abstract
To report the occurrence and management of severe infectious scleritis in a 75 year-old woman following intravitreal ranibizumab injection. A 75 year-old monocular woman receiving monthly intravitreal ranibizumab injection for wet age related macular degeneration in the left eye presented with severe dull pain, decreased vision, and scleral melt with discharge 2 weeks after her last injection. The dilated fundus exam was devoid of vitritis. The patient was admitted to our hospital for both diagnostic and therapeutic purposes. She was initially started on aggressive oral and topical antibiotics, but showed no significant improvement. The scleral cultures were positive for Pseudomonas aeruginosa. In view of the aggressive nature of her infection, intravenous antibiotics were added to the treatment regimen. The patient recovered her baseline visual function after two weeks of intravenous, oral and, topical antibiotics. To our knowledge, this is the first case of anterior infectious necrotizing scleritis secondary to Pseudomonas aeruginosa infection following intravitreal ranibizumab injection. Clinicians performing intravitreal injections should have a high index of suspicion for iatrogenic infections including scleritis and endophthalmitis, as these infections require aggressive topical and systemic antibiotics as well as possible hospitalization.
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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) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".