Retro or PeriBulbar Injection Techniques to Reverse Visual Loss After Filler Injections
Bibliographic record
Abstract
BACKGROUND: With the increasing popularity of three dimensional filler treatments, the tip of the injecting needle or cannula is subdermal, often adjacent to the facial vasculature. Inadvertent canalization of these vessels can rarely result in vascular occlusion with consequent blindness or stroke. OBJECTIVE: The goal was to demonstrate a technique of retro or peribulbar injection of hyaluronidase should such an emergency arise if no ophthalmologist was immediately available. The treatment is needed urgently because the retina can only withstand up to 90 minutes of hypoxia. MATERIALS AND METHODS: We present verbal and diagrammatic instructions for this treatment and videos of both the retrobulbar technique (courtesy of Dr. D. Maberley) and peribulbar technique (courtesy of Dr. S. Fagien). RESULTS: None of us have yet used this technique to treat iatrogenic filler induced blindness but we present the information for the benefit of patient safety, given the limited time the retina can survive vascular occlusion. CONCLUSION: Inadvertent canalization of facial vessels can cause embolic occlusion of retinal vasculature because of the anastomotic nature of the facial vasculature and the end artery morphology of the retinal circulation. Embolism from 3-dimensional hyaluronic acid filler use is potentially reversible if hyaluronidase is injected into the adjacent tissue.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".