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1000 Titel
  • A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year
1000 Autor/in
  1. Heldring, Nina |
  2. Rezaie, Ali-Reza |
  3. Larsson, André |
  4. Gahn, Rebecca |
  5. Zilg, Brita |
  6. Camilleri, Simon |
  7. Saade, Antoine |
  8. Wesp, Philipp |
  9. Palm, Elias |
  10. Kvist, Ola |
1000 Verlag Springer Berlin Heidelberg
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-09-18
1000 Erschienen in
1000 Quellenangabe
  • 139(1):197-217
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s00414-024-03324-x |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732965/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title> <jats:p>Age estimations are relevant for pre-trial detention, sentencing in criminal cases and as part of the evaluation in asylum processes to protect the rights and privileges of minors. No current method can determine an exact chronological age due to individual variations in biological development. This study seeks to develop a validated statistical model for estimating an age relative to key legal thresholds (15, 18, and 21 years) based on a skeletal (CT-clavicle, radiography-hand/wrist or MR-knee) and tooth (radiography-third molar) developmental stages. The whole model is based on 34 scientific studies, divided into examinations of the hand/wrist (15 studies), clavicle (5 studies), distal femur (4 studies), and third molars (10 studies). In total, data from approximately 27,000 individuals have been incorporated and the model has subsequently been validated with data from 5,000 individuals. The core framework of the model is built upon transition analysis and is further developed by a combination of a type of parametric bootstrapping and Bayesian theory. Validation of the model includes testing the models on independent datasets of individuals with known ages and shows a high precision with separate populations aligning closely with the model’s predictions. The practical use of the complex statistical model requires a user-friendly tool to provide probabilities together with the margin of error. The assessment based on the model forms the medical component for the overall evaluation of an individual’s age.</jats:p>
1000 Sacherschließung
lokal Adolescent [MeSH]
lokal Bayesian theorem
lokal Female [MeSH]
lokal Biological variation
lokal Forensic anthropology
lokal Humans [MeSH]
lokal Molar, Third/diagnostic imaging [MeSH]
lokal Bayes Theorem [MeSH]
lokal Molar, Third/growth
lokal Original Article
lokal Male [MeSH]
lokal Clavicle/diagnostic imaging [MeSH]
lokal Validation study
lokal Models, Statistical [MeSH]
lokal Young Adult [MeSH]
lokal Age Determination by Skeleton/methods [MeSH]
lokal Population
lokal Age Determination by Teeth/methods [MeSH]
lokal Age distribution
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-9881-8182|https://frl.publisso.de/adhoc/uri/UmV6YWllLCBBbGktUmV6YQ==|https://frl.publisso.de/adhoc/uri/TGFyc3NvbiwgQW5kcsOp|https://frl.publisso.de/adhoc/uri/R2FobiwgUmViZWNjYQ==|https://frl.publisso.de/adhoc/uri/WmlsZywgQnJpdGE=|https://frl.publisso.de/adhoc/uri/Q2FtaWxsZXJpLCBTaW1vbg==|https://frl.publisso.de/adhoc/uri/U2FhZGUsIEFudG9pbmU=|https://frl.publisso.de/adhoc/uri/V2VzcCwgUGhpbGlwcA==|https://frl.publisso.de/adhoc/uri/UGFsbSwgRWxpYXM=|https://frl.publisso.de/adhoc/uri/S3Zpc3QsIE9sYQ==
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