Causality assessment of adverse drug reaction: A narrative review to find the most exhaustive and easy-to-use tool in post-authorization settings
Bibliographic record
Abstract
BACKGROUND: The core motive of pharmacovigilance is the detection and prevention of adverse drug reactions (ADRs), to improve the risk-benefit balance of the drug. However, the causality assessment of ADRs remains a major challenge among clinicians, and none of the available tools of causality assessment used for assessing ADRs have been universally accepted. OBJECTIVE: To provide an up-to-date overview of the different causality assessment tools. METHODS: We conducted electronic searches in MEDLINE, EMBASE, and the Cochrane database. The eligibility of each tool was screened by three reviewers. Each eligible tool was then scrutinized for its domains (the reported specific set of questions/areas used for calculating the likelihood of cause-and-effect relation of an ADR) to discover the most comprehensive tool. Finally, we subjectively assessed the tool's ease-of-use in a Canadian, Indian, Hungarian, and Brazilian clinical context. RESULTS: Twenty-one eligible causality assessment tools were retrieved. Naranjo's tool and De Boer's tool appeared the most comprehensive among all the tools, covering 10 domains each. Regarding "ease-of-use" in a clinical setting, we judged that many tools were hard to implement in a clinical context because of their complexity and/or lengthiness. Naranjo's tool, Jones's tool, Danan and Benichou's tool, and Hsu and Stoll's tool appeared to be the easiest to implement into various clinical contexts. CONCLUSION: Among the many tools identified, 1981 Naranjo's scale remains the most comprehensive and easy to use for performing causality assessment of ADRs. Upcoming analysis should compare the performance of each ADR tool in clinical settings.
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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 | 0.022 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".