ReviewForensic science, medicine, and pathology2020
A review of causal inference in forensic medicine.
Review in Forensic science, medicine, and pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Enhancing quality assurance in forensic medicolegal opinions: A standardized training and auditing framework for Arab countries.Forensic science international. Synergy · 2026Article
- Can large language models serve as consultants for forensic cause of death analysis? A multidimensional evaluation.Frontiers in artificial intelligence · 2026Article
- Causal attribution in forensic psychological injury: legal causation, validity assessment, and expert reasoning.Frontiers in psychology · 2026Review
- Article
- Imaging of musculoskeletal injury: timing estimation and medico-legal issues.La Radiologia medica · 2025Review
- Postmortem metabolomics as a high-throughput cause-of-death screening tool for human death investigations.iScience · 2024Article
- A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China.PloS one · 2024Article
- Response to "Scrutinizing the causal link between excited delirium syndrome and restraint - a commentary on: 'The role of restraint in fatal excited delirium: a research synthesis and pooled analysis' by E.M.F. Strömmer, W. Leith, M.P. Zeegers and M.D. Freeman".Forensic science, medicine, and pathology · 2023Article
- Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).Annals of operations research · 2021Article
- INFERENCE: An Evidence-Based Approach for Medicolegal Causal Analyses.International journal of environmental research and public health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The primary aim of forensic medical analysis is to provide legal factfinders with evidence regarding the causal relationship between an alleged action and a harmful outcome. Despite existing guides and manuals, the approach to formulating opinions on medicolegal causal inference used by forensic medical practitioners, and how the strength of the opinion is quantified, is mostly lacking in an evidence-based or systematically reproducible framework. In the present review, we discuss the literature describing existing methods of causal inference in forensic medicine, especially in relation to the formulation of expert opinions in legal proceedings, and their strengths and limitations. Causal inference in forensic medicine is unique and different from the process of establishing a diagnosis in clinical medicine. Because of a lack of tangibility inherent in causal analysis, even the term "cause" can have inconsistent meaning when used by different practitioners examining the same evidence. Currently, there exists no universally applied systematic methodology for formulating and assessing causality in forensic medical expert opinions. Existing approaches to causation in forensic medicine generally fall into two categories: intuitive and probabilistic. The propriety of each approach depends on the individual facts of an investigated injury, disease, or death. We opine that in most forensic medical settings, probabilistic causation is the most suitable for use and readily applicable. Forensic medical practitioners need, however, be aware of the appropriate approach to causation for different types of cases with varying degrees of complexity.
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.