Adverse Event Reporting with Selective Serotonin-Reuptake Inhibitors
Bibliographic record
Abstract
OBJECTIVE: The Weber effect is a phenomenon which states that the number of reported adverse reactions for a drug increases until the middle to end of the second year of marketing. The purpose of this study was to examine the number of adverse event reports associated with specific selective serotonin-reuptake inhibitor (SSRI) use. METHODS: Data used in this study included voluntary adverse event reports submitted to the US federal government through the Spontaneous Reporting System and Adverse Event Reporting System. Adverse event reports were analyzed for the following SSRIs: citalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline. RESULTS: Adverse event reporting associated with fluvoxamine demonstrates the Weber effect. Adverse events related to fluoxetine, paroxetine, and sertraline do not exhibit the Weber effect. Fluoxetine-related adverse events peaked at year 3, with peaks also occurring during the 10th and 12th years after market entry. Adverse event reports associated with paroxetine and sertraline use increased 5-8 years after market entry. CONCLUSIONS: Within 1 class of medications, it is possible for a few agents to exhibit the Weber effect, while there is no definite pattern with others. A new observation in adverse event reporting is introduced and suggests that a peak in adverse event reporting occurs 1-2 years after a medication receives approval for a new indication. Future research is necessary to validate this effect and examine the generalizability to other medications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".