Late-onset Deep Infectious Keratitis After Descemet Stripping Endothelial Keratoplasty With Vent Incisions
Bibliographic record
Abstract
PURPOSE: To report the clinical and histopathological findings of 3 cases of late-onset deep infectious keratitis after Descemet stripping endothelial keratoplasty (DSEK) with vent incisions. METHODS: From a retrospective review of 150 consecutive patients who underwent uncomplicated DSEK with vent incisions, 3 patients developed late-onset deep infectious keratitis. RESULTS: In case 1, the patient suffered a Pseudomonas corneal ulcer at the nasal vent incision after a dacryocystorhinostomy with stent, 16 months after DSEK. In case 2, a Streptococcus pneumoniae infection developed at the inferior vent incision from a spastic entropion 3 months after surgery. In case 3, an Enterococcus faecalis corneal ulcer presented as a deep stromal abscess in the nasal vent incision 7 weeks after DSEK. All cases required full-thickness penetrating keratoplasties. Visual acuities at the last follow-up were counting fingers (case 1), 20/80 (case 2), and 20/400 (case 3). CONCLUSIONS: Vent incisions in DSEK may allow bacterial keratitis to penetrate deeply leading to aggressive keratolysis. One must be cautious in using vent incisions in patients with increased bacterial flora and patients with poor ocular surface healing from systemic, local, or mechanical conditions. If vent incisions are performed, a midperipheral oblique incision, parallel to the limbus, with meticulous detail to wound construction is recommended.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".