Long-Term Follow-Up of Pterygium Surgery Using a Conjunctival Autograft and Tissucol
Bibliographic record
Abstract
PURPOSE: To evaluate the rate of recurrence and to detect pre- or postoperative complications in pterygium surgery using fibrin glue to attach the conjunctival autograft. METHODS: Retrospective case series. In a period of 20 months, 35 patients were operated on for a pterygium using a conjunctival autograft, of which 7 patients were operated (20%) for a recurrent pterygium. The autograft was glued with Tissucol Duo 500, a human tissue glue. Follow-up was at least 1 year. RESULTS: Mean age was 50.4 years (range, 23-80 years), 18 women and 17 men. Success rate was 97.1%. In 1 patient with a primary pterygium, the lesion recurred after 4 months (2.9%). One autograft was lost on the first postoperative day. No other pre- or postoperative problems were encountered. CONCLUSIONS: The use of Tissucol fibrin glue seems to be a safe, easy, and effective technique for attaching the conjunctival autograft in pterygium surgery. The recurrence rate of the procedure is low.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".