Safety profile of antiviral medications: A pharmacovigilance study using the Italian spontaneous-reporting database
Bibliographic record
Abstract
PURPOSE: The results of an analysis of suspected antiviral-associated adverse drug reactions (ADRs) in Italy over a 22-year period are presented. METHODS: A case/non-case analysis was conducted using ADR reports compiled in the nationwide spontaneous-reporting database through September 2010. All reported events included in the analysis were evaluated and coded by drug safety experts; causality assessments were performed according to the algorithm of Naranjo et al. The association between an adverse reaction and antiviral use was assessed by estimating the reporting odds ratio (ROR), with 95% confidence interval (CI), as a measure of disproportionality. RESULTS: Overall, 863 reports of suspected ADRs involving antivirals and 42,430 reports of adverse reactions to other drugs were identified; of those events, 3.3% and 64.3% were determined to be definite or probable ADRs, respectively, and an additional 32.4% were deemed possibly drug related. Several ADRs were disproportionately associated with antivirals relative to other drugs: renal colic (ROR, 25.5; 95% CI, 13.3-49.0), lactic acidosis (ROR, 18.6; 95% CI, 9.2-37.7), depression (ROR, 18.0; 95% CI, 11.6-27.9), anemia (ROR, 15.9; 95% CI, 12.3-20.4), hallucination (ROR, 4.3; 95% CI, 2.7-7.1), neutropenia (ROR, 4.1; 95% CI, 2.9-5.8), acute renal failure (ROR, 3.9; 95% CI, 2.3-6.4), fever (ROR, 3.8; 95% CI, 2.8-5.1), hyperpyrexia (ROR, 2.9; 95% CI, 1.7-4.9), and asthenia (ROR, 1.8; 95% CI, 1.2-2.8). CONCLUSION: Analysis of data from a large Italian database showed that, among antiviral agents, the ribavirin-interferon combination, acyclovir, valacyclovir, indinavir, and zidovudine accounted for the most serious hematologic, neuropsychiatric, and renal ADRs.
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 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 | 0.005 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".