Update on Avoiding and Treating Blindness From Fillers: A Recent Review of the World Literature
Bibliographic record
Abstract
BACKGROUND: Sudden loss of vision secondary to filler treatments is a rare but catastrophic complication. OBJECTIVES: The aim of this study was to update the published cases of blindness after filler injection that have occurred since we published our review of 98 cases in 2015, and to discuss prevention and management strategies. METHODS: A literature review was performed to identify all cases of visual complications caused by filler injection identified between January 2015 and September 2018. RESULTS: Forty-eight new published cases of partial or complete vision loss after filler injection were identified. The sites that were highest risk were the nasal region (56.3%), glabella (27.1%), forehead (18.8%), and nasolabial fold (14.6%). Hyaluronic acid filler was the cause of this complication in 81.3% of cases. Vision loss, pain, ophthalmoplegia, and ptosis were the most common reported symptoms. Skin changes were seen in 43.8% of cases and central nervous system complications were seen in 18.8% of cases. Ten cases (20.8%) experienced complete recovery of vision, whereas 8 cases (16.7%) reported only partial recovery. Management strategies varied greatly and there were no treatments that were shown to be consistently successful. CONCLUSIONS: Although the risk of blindness from fillers is rare, practitioners who inject filler should have a thorough knowledge of this complication including prevention and management strategies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".