Update on Blindness From Filler: Review of Prognostic Factors, Management Approaches, and a Century of Published Cases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vision loss secondary to aesthetic filler treatment is a rare but disastrous complication. The aim of this review was to update the published cases of blindness after filler injection that have occurred since our group published reviews of 98 cases in 2015 and an additional 48 cases in 2019. A literature review was performed to identify all cases of visual complications caused by filler injection published between September 2018 and March 2023. The cases were analyzed independently and in combination with previously reviewed cases. Analyses were based on the number of cases with data available. A total of 365 new cases of partial or complete vision loss after filler injection were identified. The sites that were highest risk were the nose (40.6%), forehead (27.7%), and glabella (19.0%). The filler injected was hyaluronic acid in 79.6% of cases. The most common associated signs were ptosis (56.2%), ophthalmoplegia (44.1%), pain (31.2%), and skin changes (73.2%). Strokelike features were seen in 19.2% of cases. Of the cases reporting visual outcomes (318), 6.0% experienced complete vision recovery, 25.8% had partial improvement in visual acuity, and 68.2% had no vision recovery. Partially preserved visual acuity at onset was a significant predictor of visual improvement (P < .001). The 3 most common treatments were subcutaneous hyaluronidase at or near the filler site (70.1%), systemic steroids (57.3%), and intraarterial thrombolytic therapy (56.0%). No treatments were significantly associated with visual improvement (P > .05). Although blindness and stroke from fillers is a rare complication, practitioners who inject filler should have a thorough knowledge of prevention and management strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it