Advances in the use of adhesives in ophthalmology
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the known uses of available medical tissue adhesives in the management of diseases of the anterior segment, highlighting recent developments in the field. RECENT FINDINGS: Human fibrin glues may be used in place of cyanoacrylate tissue adhesives in the treatment of progressive corneal thinning and small perforations, potentially resulting in less corneal and conjunctival inflammatory reaction. Additional currently proposed uses of fibrin glues in ophthalmic surgery include minimizing sutures in recurrent pterygium surgery, forniceal reconstruction, amniotic membrane transplantation, and lamellar corneal grafting. SUMMARY: After reviewing the literature pertaining to the current use of tissue adhesives in ophthalmic surgery, the authors conclude that the main indication for cyanoacrylate tissue adhesives is for the treatment of progressive corneal thinning and small, uncomplicated corneal perforations. Human fibrin glues appear to be equally effective in such cases and have the advantage of biocompatibility, allowing application over a larger surface area and the use of a superficial covering layer such as amniotic membrane or conjunctiva for further reinforcement and promotion of rapid re-epithelialization. Other applications of human fibrin glues in ophthalmic surgery are evolving, but their widespread use is limited by concern over the theoretic risk of viral transmission and the complexity of their preparation and application in comparison with traditional sutures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".