Serotonin reuptake inhibiting antidepressants: A trigger for visual snow syndrome?
Bibliographic record
Abstract
BackgroundVisual snow is an abnormal visual perception, frequently occurring in the context of visual snow syndrome. Recent literature has suggested a link with serotonin reuptake inhibiting antidepressants, but there is little research on this topic.ObjectiveWe aimed to identify cases of visual snow and visual snow syndrome linked to serotonin reuptake inhibiting antidepressants to evaluate the possibility of an association.MethodsRetrospective analysis of patient adverse event reports linking serotonin reuptake inhibiting antidepressants with visual snow or visual snow syndrome using data from RxISK.org, a global database of spontaneous reports of drug-linked adverse events. Each case was subject to a causality assessment: a RxISK score of 0-4 indicates more information is required, 5-8 a likely link and ≥9 a strong possibility of a link between the medication and the symptoms.Results24 cases were identified; 16 male and 8 female patients, with a mean age of 30 years. All had visual snow, and 10 patients (42%) had visual snow syndrome. Reports originated from 8 countries and involved 10 different drugs. Symptoms began on the drug in 14 cases (58%), after reducing the dose in 6 cases (25%), and after discontinuation in 4 cases (17%). At the time of reporting, 22 patients (92%) had stopped the suspect drug but without resolution of symptoms. The mean RxISK score was 9.5 (range 2-17).ConclusionSerotonin reuptake inhibiting antidepressants may trigger visual snow and visual snow syndrome that fails to resolve or can even worsen or emerge after stopping the drug. With usage of these medications rising worldwide, cases are likely to increase. Further research is vital to understand potential mechanisms and risk factors.
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How this classification was reachedexpand
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".