Evidence map›Paper›PMID 32157581›Full record

ReviewForensic science, medicine, and pathology2020

A review of causal inference in forensic medicine.

Putri Dianita Ika Meilia, Michael D Freeman, Herkutanto, Maurice P Zeegers

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. INFERENCE: An Evidence-Based Approach for Medicolegal Causal Analyses.International journal of environmental research and public health · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Putri Dianita Ika MeiliaCare and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands. p.meilia@maastrichtuniversity.nl.
Michael D FreemanCare and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands.
HerkutantoDepartment of Forensic Medicine and Medicolegal Studies, Faculty of Medicine, University of Indonesia, Jl. Salemba Raya No. 4, Salemba, Jakarta Pusat, 10430, Indonesia.
Maurice P ZeegersCare and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, the Netherlands.

Funding

Lembaga Pengelola Dana Pendidikan 20160922049369
6 · The paper itself

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

CausalityForensic MedicineExpert TestimonyHumansProbabilityCausal inferenceCausalityForensic medicineIntuitive causationProbabilistic causation

Identifiers

PMID32157581
PMCPMC7245596

What OpenQuestion holds

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Registered trials

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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.