Development and persistence of patient-reported visual problems associated with serotonin reuptake inhibiting antidepressants
Bibliographic record
Abstract
BACKGROUND: The majority of antidepressants inhibit serotonin reuptake and include the selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and the serotonin reuptake inhibiting tricyclic antidepressants. OBJECTIVE: The objective of this study was to investigate and describe the range and impact of reported adverse visual effects linked to serotonin reuptake inhibiting antidepressants. METHODS: Using data from a global database of patient spontaneous reports of drug adverse events, we systematically identified eligible reports of visual problems linked to the use of serotonin reuptake inhibiting antidepressants. We analyzed these data using simple descriptive statistics to present the range and impact. RESULTS: We identified 124 reports of visual problems. Reports originate from 18 countries and involve 11 different drugs. The most commonly reported symptoms were vision blurred/visual acuity reduced (n = 79, 63.7%), night blindness (n = 22, 17.7%), vitreous floaters (n = 21, 16.9%), photophobia (n = 19, 15.3%), diplopia (n = 15, 12.1%), palinopsia (n = 13, 10.5%), visual field defect (n = 12, 9.7%), photopsia (n = 11, 8.9%) and visual snow syndrome (n = 11, 8.9%). 74 patients indicated that the side effect was bad enough to affect everyday activities, 62 had sought health care, and 50 indicated that their work had been affected. 49 patients reported an enduring vision problem after discontinuation of treatment. CONCLUSIONS: The data suggest that serotonin reuptake inhibiting antidepressants can produce a range of adverse effects on vision that in some cases can be long-lasting after discontinuation of the drug. Further efforts are needed to understand the mechanisms involved, the incidence among those prescribed these medications, and identify any risk or mitigation factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".