Sex estimation using continuous variables: Problems and principles of sex classification in the zone of uncertainty

Galeta, Patrik; Brůžek, Jaroslav

doi:10.1016/b978-0-12-815764-0.00016-2

Cited by 11 publications

(18 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some methods of sex estimation (e.g., Brůžek et al 2017; Santos et al 2019) use classification thresholds, that is, do not produce a dichotomic but a trichotomic output for sex estimation: either “F” (female) or “M” (male) if the target individual reaches a given confidence threshold for posterior probability (usually 95%), or “I” (indeterminate) if the posterior probabilities are too mitigated and do not allow for a sufficient confidence in decision making. This important question has recently been extensively discussed by Galeta and Brůžek (2020).…”

Section: Steps To Follow In Using Rdssmentioning

confidence: 98%

rdss: An R package to facilitate the use of Murail et al.'s (1999) approach of sex estimation in past populations

Santos

2021

Intl J of Osteoarchaeology

View full text Add to dashboard Cite

Although several reliable methods of sex estimation—usually based on the os coxae—do exist, they cannot always be applied on fragmented archeological material, so that sex must be estimated using other skeletal parts. In 1999, Murail and co‐authors proposed an approach, which they called “secondary sex diagnosis,” consisting in using those individuals whose pelvic sex could be estimated as a reference sample for the extrapelvic sex estimation of the other individuals. The computational use of this approach may be cumbersome when dealing with very fragmented material and implies many heavily repetitive tasks if performed manually. rdss is an R package with a graphical user interface that facilitates all the steps of the so‐called secondary sex diagnosis and allows an easy assessment of the quality and relevance of the statistical models built and used during this process. In the present article, a small subset of the freely available online Goldman Data Set is used to illustrate the use of this package on real osteometric data, and some methodological caveats that regularly arise with archeological samples are discussed. Finally, some terminological elements are clarified or modified from the seminal publication. Further technical documentation about rdss, including a video tutorial, is available online.

show abstract

Section: Steps To Follow In Using Rdssmentioning

confidence: 98%

rdss: An R package to facilitate the use of Murail et al.'s (1999) approach of sex estimation in past populations

Santos

2021

Intl J of Osteoarchaeology

View full text Add to dashboard Cite

show abstract

“…It is common in forensic anthropology to draw inferences using statistical models applied to anthropometric data. A recently published book on the use of statistics and probability in forensic anthropology Obertová et al [ 4 ], for instance, includes multiple chapters by different authors describing multiple statistical methods, including cluster analysis [ 5 ], logistic regression [ 6 ], and discriminant function analysis [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Galeta & Brůžek [ 7 ] reviews literature that expresses concern about a “zone of uncertainty”, see Fig. 1 .…”

Section: Introductionmentioning

confidence: 99%

“…In this example, the “zone of uncertainty” is between posterior probabilities of 0.05 and 0.95. Galeta & Brůžek [ 7 ] states that “It is a conservative approach, but it brings a high confidence of sex estimation at both the individual and the population level.” The aim is to have a high correct-classification rate (a low classification-error rate) for the bones that are classified, 2 but this comes at the cost of not classifying some bones and in fact not drawing any inference about the sex of the latter bones. Non-classification can occur in a high proportion, even the majority, of cases.…”

Section: Introductionmentioning

confidence: 99%

“…Non-classification can occur in a high proportion, even the majority, of cases. Galeta & Brůžek [ 7 ] discusses trade-off between correct-classification rate and proportion of cases not classified.

Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Calculation of likelihood ratios for inference of biological sex from human skeletal remains

Morrison

Weber

Basu

et al. 2021

Forensic Science International: Synergy

View full text Add to dashboard Cite

It is common in forensic anthropology to draw inferences (e.g., inferences with respect to biological sex of human remains) using statistical models applied to anthropometric data. Commonly used models can output posterior probabilities, but a threshold is usually applied in order to obtain a classification. In the forensic-anthropology literature, there is some unease with this “fall-off-the-cliff” approach. Proposals have been made to exclude results that fall within a “zone of uncertainty”, e.g., if the posterior probability for “male” is greater than 0.95 then the remains are classified as male, and if the posterior probability for “male” is less than 0.05 then the remains are classified as female, but if the posterior probability for “male” is between 0.05 and 0.95 the remains are not classified as either male or female. In the present paper, we propose what we believe is a simpler solution that is in line with interpretation of evidence in other branches of forensic science: implementation of the likelihood-ratio framework using relevant data, quantitative measurements, and statistical models. Statistical models that can implement this approach are already widely used in forensic anthropology. All that is required are minor modifications in the way those models are used and a change in the way practitioners and researchers think about the meaning of the output of those models. We explain how to calculate likelihood ratios using osteometric data and linear discriminant analysis, quadratic discriminant analysis, and logistic regression models. We also explain how to empirically validate likelihood-ratio models.

show abstract

Peptide analysis of tooth enamel – A sex estimation tool for archaeological, anthropological, or forensic research

Mikšı́k

Morvan

Brůžek

2023

J of Separation Science

Self Cite

View full text Add to dashboard Cite

Proteomics has become an attractive method to study human and animal material, biological profile, and origin as an alternative to DNA analysis. It is limited by DNA amplification in ancient samples and its contamination, high cost, and limited preservation of nuclear DNA. Currently, three approaches are available to estimate sex–osteology, genomics, or proteomics, but little is known about the relative reliability of these methods in applied settings. Proteomics provides a new, seemingly simple, and relatively non‐expensive way of sex estimation without the risk of contamination. Proteins can be preserved in hard teeth tissue (enamel) for tens of thousands of years. It uses two sexually distinct forms of the protein amelogenin in tooth enamel detectable by liquid chromatography‐mass spectrometry; the protein amelogenin Y isoform is present in enamel dental tissue only in males, while amelogenin isoform X can be found in both sexes. From the point of view of archaeological, anthropological, and forensic research and applications, the reduced destruction of the methods used is essential, as well as the minimum requirements for sample size.

show abstract

Sex estimation using continuous variables: Problems and principles of sex classification in the zone of uncertainty

Cited by 11 publications

References 28 publications

rdss: An R package to facilitate the use of Murail et al.'s (1999) approach of sex estimation in past populations

rdss: An R package to facilitate the use of Murail et al.'s (1999) approach of sex estimation in past populations

Calculation of likelihood ratios for inference of biological sex from human skeletal remains

Peptide analysis of tooth enamel – A sex estimation tool for archaeological, anthropological, or forensic research

Contact Info

Product

Resources

About