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The use of transition analysis in skeletal age estimation
Author(s) -
Getz Sara M.
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: forensic science
Language(s) - English
Resource type - Journals
ISSN - 2573-9468
DOI - 10.1002/wfs2.1378
Subject(s) - estimation , probabilistic logic , variation (astronomy) , trait , population , forensic anthropology , computer science , transition (genetics) , statistics , demography , artificial intelligence , biology , geography , mathematics , archaeology , engineering , gene , biochemistry , physics , systems engineering , sociology , astrophysics , programming language
The ability to produce accurate and precise age‐at‐death estimates from adult skeletal remains is critical for both forensic and bioarchaeological analyses. Despite many decades of investigation, anthropologists are still heavily reliant on methods that fail to adequately capture biological variation in skeletal aging and produce estimates that are insufficient for most applications. The Transition analysis (TA) by Boldsen et al. (2002) refers to a broad category of statistical approaches used to generate probabilistic information from binary or ordinal reference data as well as a specific method, Transition Analysis (TA) (Boldsen et al. 2002), that combines components of the cranial sutures and pelvic joints. The TA method uses free software to generate age estimates from the available trait scores, a statistical correction for the use of correlated features, and information about population mortality structure. Variations of the transition analysis approach have also applied to data from traditional age‐estimation methods, isolated areas of the skeleton, and many features distributed throughout the body. Investigations of TA and other transition analysis‐based approaches on archaeological, historic, and modern remains have demonstrated significant improvements in our ability to document skeletal variation, produce estimates in the upper half of the adult lifespan, and investigate patterns of mortality in the past. Despite these improvements, several challenges remain, including wide age intervals and systematic age‐estimation bias, particularly for individuals in the youngest and oldest portions of adulthood. Moving forward, component‐based approaches capable of producing individualized, probabilistic estimates using new skeletal features and user‐friendly software are needed. This article is categorized under: Forensic Anthropology > Age Assessment

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