Intralesional Injection of Tisseel Fibrin Glue for Resection of Lymphangiomas and Other Thin-Walled Orbital Cysts
Bibliographic record
Abstract
In Brief Purpose: Surgical removal of orbital lymphangiomas is often difficult because the capsule of these lesions is fragile, and, once violated, it tends to collapse, making identification of residual tumor difficult and dissection often incomplete. A surgical technique combining partial controlled decompression of the lesion with intralesional injection of Tisseel fibrin glue is evaluated to determine its effectiveness in resecting the lesion and preventing recurrences. Methods: This is a retrospective interventional case series. Three young patients, two with lymphangiomas and one with congenital hydrocystoma, underwent surgical resection of their thin-walled cystic lesions of the orbit with the use of intralesional injection of Tisseel fibrin glue. Resolution of the signs and symptoms, complications of the surgery, and recurrence of bleeding are the parameters studied. Results: All 3 patients had improvement of their signs and symptoms. None had complications related to the surgery, and no recurrence of bleeding occurred during the follow-up period. Conclusions: Our study suggests that this surgical technique with intralesional injection of Tisseel fibrin glue is an effective treatment modality for the resection of lymphangiomas and other orbital thin-walled cystic lesions. Partial controlled decompression of the lesion combined with intralesional injection of Tisseel fibrin glue was effective in resecting 3 thin-walled cystic lesions of the orbit including 2 lymphangiomas, with resolution of signs and symptoms and no surgical complications or recurrence of bleeding.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".