Local Papaverine as a Novel Adjunct for Delayed Vision Loss after Tuberculum Sellae Meningioma Resection
Bibliographic record
Abstract
Context: Tuberculum sellae meningiomas (TSM) are in close anatomic proximity to the optic nerves and frequently compress them as they grow. Patients often present with decreased visual acuity and visual field defects, necessitating surgical intervention. A rare but severe complication following TSM surgery is delayed vision loss after an uncomplicated immediate postoperative period. The exact mechanism of this complication remains unknown, but ischemic injury is hypothesized to be the primary pathological process. Arterial vasospasm, venous congestion, and reperfusion injury are thought to be major contributors to this condition. Current therapeutic options are limited, with no proven efficacy. This lack of established treatments underscores the need for innovative approaches to manage this challenging complication. Methods: We present our experience with the use of local papaverine in two cases of delayed vision loss following endoscopic endonasal removal of TSM. Results: The patients experienced delayed vision loss at 12 and 16 hours postoperatively. Both underwent surgical field reexploration on their first postoperative day and received intraoperative local papaverine deposition on the optic nerve. Following local papaverine administration, both patients demonstrated immediate postoperative improvement. They also underwent hyperdynamic therapy, chemical angioplasty with verapamil, and corticosteroid taper. Conclusion: Vasospasm is suspected to be a major contributor to delayed vision loss. In both patients, we observed improved visual function after local antispasmodic (papaverine) therapy as part of a multimodal treatment. The inflammatory cascade, venous congestion, and surgical site edema likely contributed to the delayed presentation of this complication. We report our experience with local papaverine irrigation as an adjunctive and potentially effective treatment for this condition. This novel treatment option reinforces the current understanding of the complication and introduces a new therapeutic alternative. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".