Determination of age at death in human skeletal remains: A comparison of two techniques

Gillett, Rhonda M.

doi:10.1007/bf02443951

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…According to Katz and Suchey (4) the problem with the original Todd method is that it overestimates the age‐at‐death for most individuals, particularly those under the age of 40 years; does not account for individual variation; and does not accurately age older individuals. These researchers, along with many others, e.g., Gillet (5), Galera et al. (6), and Kemkes‐Grottenthaler (7), found that the modified six‐phase system is a more accurate estimator than the original Todd system.…”

mentioning

confidence: 89%

Analysis of Age‐at‐Death Estimation Through the Use of Pubic Symphyseal Data*

Kimmerle

Konigsberg

Jantz

et al. 2008

Journal of Forensic Sciences

105

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The question of whether age parameters derived from an American population will reliably estimate age-at-death for East European skeletal populations is important since the ability to accurately estimate an individual's age-at-death hinges on what standard is used. A reference sample of identified individuals with known ages-at-death from the regions of the Former Yugoslavia (n = 861) is used to determine the age structure of victims and serves as the prior in the Bayesian analysis. Pubic symphyseal data in the manners of Todd (Am J Phys Anthropol, 3 [1920], 285; Am J Phys Anthropol, 4 [1921], 1) and Suchey-Brooks (Am J Phys Anthropol, 80 [1986], 167) were collected for n = 296 Balkan males and females and for n = 2078 American males and females. An analysis of deviance is calculated using an improvement chi-square to test for population variation in the aging processes of American and East European populations using proportional odds probit regression. When males and females are treated separately, there is a significant association among females and the population (df = 1, chi-square likelihood ratio = 15.071, p = 0.001). New age estimates for Balkan populations are provided and are based on the calculated age distribution from the Gompertz-Makeham hazard analysis and the ages-of-transition. To estimate the age-at-death for an individual, the highest posterior density regions for each symphyseal phase are provided.

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mentioning

confidence: 89%

Analysis of Age‐at‐Death Estimation Through the Use of Pubic Symphyseal Data*

Kimmerle

Konigsberg

Jantz

et al. 2008

Journal of Forensic Sciences

105

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“…The Suchey-Brooks method is one of the most reliable methods of analyzing the pubic symphysis. Besides having the same macroscopic morphological subdivided principle as Todd's method, Suchey-Brooks' method studied both male and female samples and added the process of completion of the ventral rampart [10,11]. The two methods have their own shortcomings.…”

Section: Discussionmentioning

confidence: 98%

Determination of male age at death in Chinese Han population: Using quantitative variables statistical analysis from pubic bones

Chen

Zhang²,

Tao

2008

Forensic Science International

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“…21 However, in the years that followed, investigations undertaken with the Suchey–Brooks method using different population groups demonstrated varying applicability. 9,22–72 Moreover, despite being one of the most commonly utilized age estimation methods, investigations evaluating the forensic suitability of the Suchey–Brooks method for ageing an Indian population are currently limited. 73…”

Section: Introductionmentioning

confidence: 99%

Applicability of the Suchey–Brooks method for age estimation in an Indian population: A computed tomography-based exploration using Bayesian analysis and machine learning

et al. 2023

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Age estimation occupies a prominent niche in the identification process. In cases where skeletal remains present for examination, age is often estimated from markers distributed throughout the skeletal framework. Within the pelvis, the pubic symphysis constitutes one of the more commonly utilized skeletal markers for age estimation, with the Suchey–Brooks method comprising one of the more commonly employed methods for pubic symphyseal age estimation. The present study was targeted towards assessing the applicability of the Suchey–Brooks method for pubic symphyseal age estimation, an aspect largely unreported for an Indian population. In order to do so, clinically undertaken pelvic computed tomography scans of individuals were evaluated using the Suchey–Brooks method, and the error associated with the method was established using Bayesian analysis and different machine learning regression models. Amongst different supervised machine learning models, support vector regression and random forest furnished lowest error computations in both sexes. Using both Bayesian analysis and machine learning, lower error computations were observed in females, suggesting that the method demonstrates greater applicability for this sex. Inaccuracy and root mean square error obtained with Bayesian analysis and machine learning illustrates that both statistical modalities furnish comparable error computations for pubic symphyseal age estimation using the Suchey–Brooks method. However, given the numerous advantages associated with machine learning, it is recommended to use the same within medicolegal settings. Error computations obtained with the Suchey–Brooks method, regardless of the statistical modality utilized, indicate that the method should be used in amalgamation with additional markers to garner accurate estimates of age.

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Determination of age at death in human skeletal remains: A comparison of two techniques

Cited by 3 publications

References 10 publications

Analysis of Age‐at‐Death Estimation Through the Use of Pubic Symphyseal Data*

Analysis of Age‐at‐Death Estimation Through the Use of Pubic Symphyseal Data*

Determination of male age at death in Chinese Han population: Using quantitative variables statistical analysis from pubic bones

Applicability of the Suchey–Brooks method for age estimation in an Indian population: A computed tomography-based exploration using Bayesian analysis and machine learning

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