Evidence map›Paper›PMID 36353333›Full record

ArticleForensic sciences research2022

Missing and murdered indigenous women and girls in Canada: a new population affinity assessment technique to aid in identification using 3D technology.

Elisabeth Cuerrier-Richer

Abstract read
In one paragraph

Article in Forensic sciences research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

1 author.

Elisabeth Cuerrier-RicherDepartment of Anthropology, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0002-9022-4686

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As of 2015, 204 cases of missing and murdered Indigenous women and girls (MMIWG) remained unsolved in Canada, making it a major concern for Canadian Indigenous communities, who are still pressing for the resolution of these cases. In forensic anthropology, the assessment of population affinity can be useful to help identify victims. Population affinity, previously referred to as ancestry, is evaluated based on morphological analyses, which examine the size and shape of skeletal features, and metric analyses, which utilise skeletal measurements. However, morphological analyses strongly depend on an anthropologist's experience with human variation, which makes the analyses particularly challenging to reproduce and standardise. The purpose of this study is to improve the rigour of morphological analyses by using 3D technology to quantify relevant cranial nonmetric population affinity traits. As there is currently little morphological data available for the Canadian Indigenous population, this research aims to develop a new technique that could aid in the identification of MMIWG. The study comprised a total of 87 adult female crania, including 24 of Canadian Inuit origin, 50 of European descent and 13 of African descent. The samples were imaged using photogrammetry, then analysed using a 3D shape analysis in 3DS Max. Results show that this method is satisfactory in correctly evaluating population affinity with an accuracy of 87.36% (jackknifed: 80.46%) and an average repeatability of 97%. Unfortunately, the small Canadian Indigenous sample size impacted the applicability of the results and further research will be required before the technique can be used to aid in the identification of MMIWG in Canada.

Indexed as

3D shape analysisCanadian Indigenouscranial traitsforensic anthropologyForensic sciencesmorphological analysespopulation affinity assessment

Identifiers

PMID36353333
PMCPMC9639536

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.