Antineoplastic Drug-induced Aortitis: An Unraveled Adverse Effect Using the World Health Organization Pharmacovigilance Database
Bibliographic record
Abstract
To the Editor: Aortitis is a rare inflammatory disease ranging from asymptomatic aortic thickening to life-threatening manifestations, especially aortic dissection or stenosis. Aortitis mainly occurs during systemic inflammatory diseases (giant cell arteritis, Takayasu arteritis, IgG4-related disease) and less frequently in patients with syphilis or tuberculosis1. Aortitis is rarely suspected to be induced by drugs and its causality is hardly assessable. The aim of our study is to identify drugs associated with aortitis occurrence using a data-mining approach. We used VigiBase, the World Health Organization (WHO) global Individual Case Safety Report (ICSR) database, which contains reports of suspected adverse drug reactions (ADR) collected by national drug authorities in more than 130 countries. The database is automatically deduplicated by VigiBase; further, a case-by-case review has been performed to exclude possible duplicates. This makes it powerful for the conduct of disproportionality analyses. This pharmacovigilance statistical method, based on a case/non-case approach, estimates whether an adverse event is differentially reported for a drug compared to other drugs. The association can be expressed using the reporting OR (ROR) and its CI for each drug adverse event combination. This approach has proven its interest for the detection of safety signals2. To identify drugs associated with aortitis occurrence, we extracted ICSR recorded in VigiBase from inception in 1967 until June 30, … Address correspondence to Prof. B. Terrier, Department of Internal Medicine, Hôpital Cochin, 27, rue du Faubourg Saint-Jacques, 75679 Paris Cedex 14, France. E-mail: benjamin.terrier{at}aphp.fr
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 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".