Amniotic Membrane Transplantation and Fibrin Glue in the Management of Corneal Ulcers and Perforations
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy of amniotic membrane in corneal ulcers refractive to conventional treatment and amniotic membrane with fibrin glue in corneal perforations. METHODS: Amniotic membrane transplantation (AMT) was performed in 33 eyes from 32 patients for corneal ulcers refractive to conventional treatment. Fourteen ulcers were perforated and received fibrin glue and amniotic membrane. Ulcers were divided into 3 groups: neurotrophic or exposure, autoimmune, and other etiology. RESULTS: Overall success was observed in 80% (27/33 eyes) of the cases, with success rates of 87.5% (14/16 eyes), 70% (7/10 eyes), 85.7% (6/7 eyes) in groups 1, 2, and 3, respectively. The ulcers healed in a mean time of 3.6 +/- 1.6 weeks and the follow-up was 14.8 +/- 9.9 months. Failure was noted in 6 eyes with severe neurotrophic keratitis, Stevens-Johnson syndrome, ocular cicatricial pemphigoid, and Acanthamoeba keratitis. Grafts with fibrin sealant showed a success rate of 92.9 % (13/14 eyes) compared to 73.7% (14/19 eyes) for amniotic grafts alone. In patients with severe limbal damage, a success rate of only 20% (1/5) was observed. CONCLUSIONS: AMT is a viable option in the treatment of nonhealing corneal ulcers of various depth and etiologies. Perforations up to 3 mm can be safely managed by fibrin glue and AMT. These techniques lead to rapid reconstruction of the corneal surface and can give a good final functional result or allow keratoplasty to be done in more favorable conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".