ArticleInternational journal of clinical pharmacy2018
Comparison of different methods for causality assessment of adverse drug reactions.
Article in International journal of clinical pharmacy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 87 citations in OpenAlex.
- Risk prediction models for adverse drug reactions and adverse drug events in older adults-a systematic review and meta-analysis.European journal of clinical pharmacology · 2025Pooled it
- Sports nutrition supplements and adverse events - a meta-epidemiological study of case reports specifically addressing causality assessment.European journal of clinical pharmacology · 2022Pooled it
- Anti-PD-L1 antibody ASC22 in combination with a histone deacetylase inhibitor chidamide as a "shock and kill" strategy for ART-free virological control: a phase II single-arm study.Signal transduction and targeted therapy · 2024Trial
- Incidence of Adverse Drug Reactions in COVID-19 Patients in China: An Active Monitoring Study by Hospital Pharmacovigilance System.Clinical pharmacology and therapeutics · 2020Trial
- Reporting patterns of adverse drug withdrawal events using individual case safety reports in the FAERS and Eudravigilance databases.Exploratory research in clinical and social pharmacy · 2026Article
- Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance.Briefings in bioinformatics · 2026Article
- Toward more accurate adverse event attribution in multiple myeloma clinical trials.Blood cancer journal · 2026Review
- Effectiveness, safety, and pharmacoeconomic profile of Sophora flavescens film in the treatment of postpartum vaginal dysbiosis: a non-randomized controlled trial.International journal of clinical pharmacy · 2026Article
- Drug-induced psoriasis following sacubitril/valsartan: a case report and literature review.BMC cardiovascular disorders · 2026Review
- Article
- A descriptive, retrospective analysis of the quality of spontaneous adverse event reporting to the Côte d'Ivoire Pharmaceutical and Regulatory Authority and its impact on the causality assessment.Therapeutic advances in drug safety · 2026Article
- Valproic Acid-Induced Pancreatitis in a Pediatric Patient With 17q12 Duplication Syndrome.Cureus · 2026Article
- Case Report: FOLFOX-induced vulvar mucositis: a rare side effect of 5-FU.Frontiers in oncology · 2026Article
- Modified sini powder for the management of postoperative depression in non-small cell lung cancer patients: a multicenter, randomized, double-blind, placebo-controlled trial protocol.Frontiers in pharmacology · 2026Article
- Assessing antimicrobial utilization and costs, and correlating biological markers with adverse events in ICU patients: An effort towards antimicrobial stewardship.Journal of family medicine and primary care · 2025Article
- Agreement and correlation between WHO-UMC Causality scale and the Naranjo algorithm for causality assessment of adverse drug reactions at tertiary care center in Northern India.Journal of family medicine and primary care · 2025Article
- Identification of Risk Factors and Predictive Indicators for Tigecycline-Associated Hypofibrinogenemia.Clinical and translational science · 2025Observational
- Serious Adverse Events: A Replicability and Validation Study of Naranjo Causality Assessment Tool in a Canadian Clinical Setting.Journal of evaluation in clinical practice · 2025Article
- An Integrated Approach for Representing Knowledge on the Potential of Drugs to Cause Acute Kidney Injury.Drug safety · 2025Article
- Applying the Lasso Model to Identify Risk Factors for Piperacillin-Tazobactam-Induced Leukopenia.Drug design, development and therapy · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background The causality assessment of adverse drug reactions (ADRs) remains a challenge, and none of the different available method of causality assessment used for assessing adverse reactions has been universally accepted as the gold standard. Objective To examine the agreement and correlation among three broad approaches for causality assessment of ADRs viz. World Health Organization-Uppsala Monitoring Centre (WHO-UMC) system, Naranjo algorithm, and updated Logistic method. Setting ADR monitoring centre (AMC) of a tertiary care teaching hospital in India. Method A total of 230 cases of ADR from April 2017 to August 2017 were retrospectively analyzed by each of these three methods. The agreement among the different methods was calculated by Cohen's kappa (κ), and Spearman's correlation was used to find the correlation among these methods. Main outcome measures Cohen's kappa value and Spearman's correlation coefficient for comparison among the different methods. Results The Cohen's κ used for analyzing the agreement between WHO-UMC system and Naranjo algorithm was 0.45, between WHO-UMC system and updated Logistic method was 0.405, and between Naranjo algorithm and updated Logistic method was 0.606. The Spearman's correlation coefficient was 0.793 for Naranjo algorithm vs. updated Logistic method, 0.735 for WHO-UMC system vs. Naranjo algorithm, and 0.696 for WHO-UMC system vs. updated Logistic method. Conclusion Causality assessment based on objective measurements (scores and probabilities) like updated Logistic method and Naranjo algorithm are less prone to subjective variations compared to the WHO-UMC system which is based on expert judgement.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.