ENDOGENOUS FUNGAL ENDOPHTHALMITIS AFTER COVID-19 INFECTION: CASE REPORT AND REVIEW OF LITERATURE
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe a case of endogenous endophthalmitis (EE) after severe COVID-19 disease, review patient outcomes with EE after COVID-19 infection, and review evidence regarding risk factors for developing EE. METHODS: This is a review of health records, imaging, intravitreal injection, and pars plana vitrectomy for bilateral fungal EE after severe COVID-19 disease, and is a literature review on outcomes in EE after COVID-19 disease. RESULTS: Sixty-three year-old man with diabetes and hypertension was admitted to hospital for severe COVID-19 disease for 3 months. His stay required intensive care unit admission, intubation, high-dose corticosteroids, tocilizumab, and was complicated by bacteremia, empyema, and fungal esophagitis. He developed floaters and bilateral vision loss (visual acuity 20/40 in the right eye, counting fingers in the left eye) with vitritis 2.5 months into his stay that did not respond to intravitreal voriconazole. Pars plana vitrectomy was performed for both eyes, resulting in visual acuity of 20/40 in the right eye, 20/30 in the left eye. Vitreous cultures were positive for Candida albicans . Endogenous endophthalmitis after COVID-19 disease has been reported in 22 patients to date, and outcomes are poor, with 40%+ of eyes legally blind (20/200 or worse). Although influenced by availability of imaging modalities and degree of training of the evaluating physician, misdiagnosis can affect ¼ of cases, delaying treatment. Age, male sex, and diabetes increase the risk of severe COVID-19, which requires prolonged hospitalization, invasive catheterization, and immunosuppression, which in turn increases the risk of nosocomial infection. CONCLUSION: Low threshold for suspecting EE in patients presenting with floaters and decreased vision after severe COVID-19 disease is necessary to ensure prompt recognition and treatment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".