Drug-associated valvular heart diseases and serotonin-related pathways: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Serotonergic appetite suppressants and ergot-derived dopamine agonists have been associated with drug-induced valvular heart disease. The purpose of this meta-analysis is to synthesise the current evidence of a link between several medications affecting sertonergic pathways and valvular heart disease. METHODS: PubMed was searched to identify studies evaluating an association between medications with serotonergic activity and cardiac valvular pathology. Case reports, uncontrolled studies and in vitro studies were excluded. Relevant studies were assessed for quality and potential bias; those of adequate quality were included in a quantitative synthesis. Sensitivity analyses were conducted, and potential publication bias was examined. RESULTS: There was a consistent, significant relationship between certain medications and heart valve disease, including serotonergic medications (OR 3.30, 95% CI 1.99 to 5.49) and dopaminergic medications (OR 2.56, 95% CI 1.68 to 3.91). Subanalyses, including analyses that limited exposure to a single medication or effects to a single heart valve were also consistently significant. Most studies were retrospective or observational in nature, with a higher risk of selection and presentation biases. There was significant heterogeneity and variability between studies, particularly when it came to dose and duration of exposure. CONCLUSIONS: There was a consistent, significant association between many medications that affect serotonergic pathways and valvular heart disease. Although many of these medications have been withdrawn from the market, some small studies suggest that recreational drug 3,4-methylenedioxymethamphetamine and widely prescribed selective serotonin reuptake inhibitors may affect similar pathways.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.064 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".