A Case of Pseudomonas Orbital Cellulitis Following Glaucoma Device Implantation
Bibliographic record
Abstract
PURPOSE: Orbital cellulitis is a rare complication of aqueous tube shunt surgery. Nine cases have been described in the literature, though the microbiologic etiology is rarely reported. Management with intravenous antibiotics and/or explantation has been described. METHODS: This is a case report and literature review. CASE: A 64-year-old woman developed pain, periorbital swelling, limited extraocular motility, proptosis, and conjunctival injection 3 days following implantation of an Ahmed Glaucoma Valve. Computed tomography of the orbits with contrast showed soft tissue fat stranding consistent with orbital inflammation. Initial medical management with topical and intravenous ceftriaxone and vancomycin was unsuccessful. Surgical removal of the implant was performed and intraoperative cultures demonstrated florid Pseudomonas aeruginosa growth. Antibiotic coverage was changed to Piperacillin-Tazobactam for 3 days, with eventual resolution of her orbital symptoms. CONCLUSIONS: We report the first case of orbital cellulitis after implantation of a glaucoma device associated with P. aeruginosa. Failure of intravenous and topical antibiotics led to explantation of the valve and targeted intravenous antibiotic therapy with subsequent clinical improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".