ArticlePharmaceuticals (Basel, Switzerland)2024
Use of Exposure Data to Establish Causality in Drug-Adverse Event Relationships: An Example with Desvenlafaxine.
Article in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors at 2 institutions in 1 country.
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Abstract
Causality algorithms help establish relationships between drug use and adverse event (AE) occurrence. High drug exposure leads to a higher likelihood of an AE being classified as an adverse drug reaction (ADR). However, there is a knowledge gap regarding what concentrations are predictive of ADRs, as this has not been systematically studied. In this work, the Spanish Pharmacovigilance System (SEFV) algorithm was used to define the relationship between the AE occurrence and drug administration in 178 healthy volunteers participating in five desvenlafaxine single-dose clinical trials, a selective serotonin and norepinephrine reuptake inhibitor that may cause dizziness, headache, nausea, dry mouth, constipation and hyperhidrosis. Eighty-three subjects presented 172 AEs that were classified as possible (101), conditional (31), unrelated (24) and probable (16). AUC
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