SSRI Treatment—Associated Stroke: Causality Assessment in Two Cases
Bibliographic record
Abstract
OBJECTIVE: To assess the probability of cerebrovascular adverse drug reactions (CV-ADRs) due to treatment with selective serotonin-reuptake inhibitors (SSRIs) using 2 causality methods. case summaries: Two patients with the possibility of SSRI-related stroke were referred for causality assessment. Causality assessment was performed using an adverse drug reaction probability scale, as well as clinical and radiologic parameters. A 31-year-old white man, who had been receiving paroxetine 200 mg/day over a period of 3 years, developed ischemic stroke involving left middle cerebral artery. The second patient was a 46-year-old white woman with a history of recurrent depression who developed delirium and ischemic stroke while she was taking a combination of paroxetine 50 mg/day, trazodone 200 mg/day, and bupropion 150 mg/day. DISCUSSION: Carotid and cardiothromboembolism were found to be the major etiological factors for ischemic stroke. Accounting for the temporal relation, prior reports of SSRI treatment-associated CV-ADRs, and the pharmacologic action of serotonin on coagulation and the vascular system, the possible contribution of SSRIs to stroke in these patients was considered. An objective causality assessment using the Naranjo probability scale revealed that a CV-ADR was possible. However, the nature of the stroke, plus clinical and radiologic findings, were inconsistent with known pathophysiologic mechanisms linking SSRIs and stroke in these patients. CONCLUSIONS: Causality assessment may improve unbiased recognition, management, and voluntary reporting of infrequent adverse effects such as SSRI treatment-related cerebrovascular accident.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".