A Sex- and Gender-Based Analysis of Adverse Drug Reactions: A Scoping Review of Pharmacovigilance Databases
Bibliographic record
Abstract
Drug-related adverse events or adverse drug reactions (ADRs) are currently partially or substantially under-reported. ADR reporting systems need to expand their focus to include sex- and gender-related factors in order to understand, prevent, or reduce the occurrence of ADRs in all people, particularly women. This scoping review describes adverse drug reactions reported to international pharmacovigilance databases. It identifies the drug classes most commonly associated with ADRs and synthesizes the evidence on ADRs utilizing a sex- and gender-based analysis plus (SGBA+) to assess the differential outcomes reported in the individual studies. We developed a systematic search strategy and applied it to six electronic databases, ultimately including 35 papers. Overall, the evidence shows that women are involved in more ADR reports than men across different countries, although in some cases, men experience more serious ADRs. Most studies were conducted in higher-income countries; the terms adverse drug reactions and adverse drug events are used interchangeably, and there is a lack of standardization between systems. Additional research is needed to identify the relationships between sex- and gender-related factors in the occurrence and reporting of ADRs to adequately detect and prevent ADRs, as well as to tailor and prepare effective reporting for the lifecycle management of drugs.
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 | 0.021 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.037 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".