Topical Application of Bevacizumab as an Adjunct to Recurrent Pterygium Surgery
Bibliographic record
Abstract
PURPOSE: To evaluate the clinical outcomes of topical bevacizumab as adjunctive to the surgical excision for treatment of recurrent pterygium. METHODS: The 44 patients who underwent recurrent pterygium excision with conjunctival autograft transplantation were enrolled in this prospective and interventional study. The patients were randomized in 2 groups: study group, 1 month after surgery, 22 patients received only topical bevacizumab (5 mg/mL) 4 times daily for 2 months; control group, 1 month after surgery, 22 patients were administered only an artificial tear 4 times daily for 2 months. Patients were prospectively followed for 6 months after pterygium surgery. The main outcome measures were pterygium recurrence, corneal neovascularization, and other postoperative complications. RESULTS: No statistically significant differences were observed between the 2 groups in terms of age, gender distributions, laterality, length of pterygium, and number of recurrence. A pterygium recurrence was not noted in any patients in the study group but was noted in 2 of 22 eyes (9%) in the control group during the follow-up after surgery (P = 0.244). Although none of the patients developed corneal neovascularization in the study group, 5 of 22 eyes (22.7%) developed this condition in the control group during the follow-up after surgery (P = 0.024). No serious complications were observed during the follow-up period in both the groups. CONCLUSIONS: Topical bevacizumab therapy 1 month after surgical excision of recurrent pterygium is well tolerated and effective to prevent neovascularization. Although the recurrence rate is lower in the study group without significant difference, further studies are required to support this result.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".