Fibrin glue versus sutures for attaching the conjunctival autograft in pterygium surgery: a prospective observer masked clinical trial
Bibliographic record
Abstract
AIMS: To compare the degree of conjunctival autograft inflammation, subconjunctival haemorrhage (SCH) and graft stability following the use of sutures or fibrin glue (FG) during pterygium surgery. METHODS: Prospective, observer masked, clinical trial. 40 eyes of 40 patients undergoing primary pterygium surgery with conjunctival autograft were allocated into two groups. Group 1 (n = 20) had FG (Tisseel) for attaching the conjunctival autograft, whereas group 2 (n = 20) had sutures. Standardised digital slit-lamp photographs were taken at 1 week, 1 month and 3 months postoperatively. Sutures were masked using commercially available photo-editing software. Two masked observers objectively graded the digital photographs for degree of inflammation, SCH and graft stability. RESULTS: 34 of the 40 patients completed the study. When using FG, the degree of inflammation was significantly less than with sutures at 1 month (p = 0.019) and 3 months (p = 0.001) postoperatively. No significant difference was found for inflammation at 1 week postoperatively (p = 0.518). Conjunctival grafts secured with FG were as stable as those secured with sutures (p = 0.258, p = 0.076 and p = 0.624, at 1 week, 1 month and 3 months, respectively). No significant difference was found in degree of postoperative SCH between the groups (p = 0.417, p = 1 and p = 1, at 1 week, 1 month and 3 months, respectively). CONCLUSION: This is the first prospective clinical trial confirming that conjunctival grafts secured with FG during pterygium surgery not only are as stable as those secured with sutures, but also produce significantly less inflammation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".