Sub-adult aging method selection (SAMS): A decisional tool for selecting and evaluating sub-adult age estimation methods based on standardized methodological parameters

Objectives: A decisional tool was developed to select sub-adult age estimation methods referenced in a centralized database. Through a freely accessible webpage interface, this tool allows users to evaluate how much the sampling and statistical protocols of these referenced methods comply with methodological recommendations published for building and applying methods in forensic anthropology. Materials and methods: 261 publications on sub-adult age estimation were collected. Three search parameters describing the anatomical element(s) and the indicators used to obtain age estimates are chosen by the user to filter the database and present the publications that best correspond to the user's selection. A simple algorithm was created to score age estimation methods according to their relevance and validity. Relevance and Validity parameters indicate how much a publication complies with user queries and published methodological recommendations, respectively; Score is a combination of Relevance and Validity. The closer these parameters are to 1, the better the method complies with the user's choice and standardized protocols. Results: The publications resulting from the user's query appear as search results alphabetically. They are characterized by their Relevance, Validity and Score values and descriptors relating to their methodology, sampling and statistical protocols. The reference of the publications and an URL to access them online are also provided. Conclusions: SAMS is a decisional tool based on a centralized database for selecting, accessing and evaluating sub-adult age estimation methods based on published methodological recommendations. Protocol validity can be easily and fully accessed to provide the necessary information for method evaluation. The database will be gradually updated and implemented as new sub-adult age estimation methods are made available online. (C) 2019 Elsevier B.V. All rights reserved.