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Metric analysis of skeletal material is integral to the analysis and identification of human remains, though one commonly used measuring device, the osteometric board, has lagged in recent advancement. Traditional boards are bulky and require manual measurement recording, potentially generating intra- and interobserver error. To address these limitations, we tested the reliability, validity, and error rates of a novel device, the Portable Osteometric Device Version 1 (PODv1), which measures distance using laser sensors with time-of-flight technology. Forty-five volunteers measured four skeletal elements with the PODv1 and a PaleoTech osteometric board in three rounds. Comparison of tibia, humerus, and femur measurements with both devices showed no significant differences, although the maximum length of the ulna did differ, potentially because of observer confusion regarding the PODv1's user instructions for this element. Our results suggest that the PODv1 is a reliable, valid measurement device compared to traditional osteometric boards. Although both device types can produce calibration, transcription, and observer errors, the time-of-flight technology and the absence of manual recording built into the PODv1 may limit those errors. These advancements and their potential positive impacts on the accuracy of osteometric data collection may have far-reaching benefits for osteological, bioarchaeological, paleopathological, and forensic anthropological data collection. El análisis métrico del material esquelético es integral para el análisis e identificación de restos humanos, aunque uno de los dispositivos de medición más comúnmente utilizados, la tabla osteométrica, ha quedado rezagada en los avances recientes. Las tablas tradicionales son voluminosas y requieren la medición manual, lo que puede generar errores intra e inter-observador. Para abordar estas limitaciones, probamos la confiabilidad, validez y tasas de error de un nuevo dispositivo, el Dispositivo Osteométrico Portátil Versión 1 (PODv1), que mide la distancia utilizando sensores láser con tecnología de tiempo de vuelo. Cuarenta y cinco voluntarios midieron cuatro elementos esqueléticos con el PODv1 y una tabla osteométrica PaleoTech en tres rondas. La comparación de las medidas de la tibia, el húmero y el fémur con ambos dispositivos no mostró diferencias significativas, aunque la longitud máxima de la ulna difirió entre ellos, posiblemente debido a la confusión del observador en torno a las instrucciones de uso del PODv1 para este elemento. Los resultados sugieren que el PODv1 es un dispositivo de medición confiable y válido en comparación con las tablas osteométricas tradicionales. Aunque ambos tipos de dispositivos pueden implicar errores de calibración, transcripción y observación, la tecnología de tiempo de vuelo y la ausencia de necesidad de registro manual incorporadas en el PODv1 pueden limitar estos problemas. Estos avances y sus posibles impactos positivos en la precisión de la recopilación de datos osteométricos pueden tener beneficios de largo alcance para la recopilación de datos osteológicos, bioarqueológicos, paleopatológicos y antropológicos forenses.

Determining whether antimeres are consistent with each other is important in estimating the minimum number of individuals in an assemblage, and this is often done with a simple visual comparison. When there are a small number of individuals, this task is generally straightforward and quick. However, as the number of individuals increases, the ability to make visual comparisons of all elements becomes cumbersome. In these cases, osteometric sorting has become a useful tool for creating shortlists of possible antimeres. Shortlists can save time by providing a smaller list of antimeres to visually compare. However, the risk of the true pair being rejected or not being reflected on the shortlist is a concern. Therefore, this study seeks to determine which statistical methods reject true pairs the least often and how these methods compare to visual pair matching. The default statistical parameters set by OsteoSort that utilize the mean of the summed left–right differences (D) performed better than using the mean of the summed absolute left–right differences (| D |). Utilizing specific measurement combinations in osteometric sorting tests, rather than all possible measurements, was only partially more successful. The true pair was excluded or rejected in statistical tests more often than true pairs being missed in a visual pair matching (VPM) study. Therefore, it is recommended to conduct VPM if the time, space, and resources allow.

•The metric validation was shown to be highly efficient and practical.•The assertiveness index was 90.57% for males and 86% for female.•DSP2 can be applied to a miscegenated population. The hip bone (os coxae) is the skeletal element that presents the greatest level of sexual dimorphism. Therefore, methods involving the analysis of the os coxae provide the most accurate sex estimation, and DSP2 (Diagnose Sexuelle Probabiliste v.2) is one of the most accurate tools used in this identification. The goal of this study is to apply and validate DSP2 in the identification of 103 os coxae (53 male and 50 female) belonging to a Brazilian-identified skeletal collection. Differences between sexes were statistically significant for all measurements, except for the acetabulo-symphyseal and spino-auricular lengths. From the 103 os coxae analyzed, there was a 9.43% error in male individuals and a 14% error in females. The results revealed that DSP2 can be applied to Brazilian-mixed populations with a good index of accuracy, although at a lower accuracy than other population samples. This study also clearly demonstrates that metric variation of the os coxae is extremely useful in sex estimation and reinforces the notion that pelvic sexual dimorphism is not population-specific.

•First postcranial metric sex standards presented for a Colombian population.•Results include uni- and multivariate equations to apply to varied forensic cases.•Best univariate measurement is scapula height; best multivariate bone is scapula.•Results similar to North American Hispanic sample, though some variation. This research explores the best univariate and multivariate indicators for sex estimation using 51 standard osteometric measurements of all six major postcranial long bones, bones of the shoulder girdle, pelvic girdle, and the calcaneus from a modern, Colombian skeletal collection. The hypotheses being tested are (1) that postcrania will yield accurate sex classification rates and (2) the shoulder girdle will demonstrate the highest discrimination, based on results from previous research. The sample consists of 134 individuals (50 females, 84 males) between the ages of 19 and 93 with a mean age of 47 years. The sample is from the Colombian Skeletal Collection, consisting of skeletons from cemeteries in Bogotá, with recent years of death. The methods include univariate and multivariate discriminant function analysis (DFA). The results for this sample indicate the same general pattern of univariate classification effectiveness as found in research on North Americans; however, here the humerus performs better than the distal femur and proximal tibia as demonstrated in some North American samples. The cross-validated percent correct univariate classification for the postcranial elements ranges from 64.8% to 86.1% (p<0.05). The highest univariate classification rates were for the scapular height (86.1%) and the humeral head diameter (86.0%). The highest multivariate classification rates are with the scapula (93.5%), os coxa (92.0%), clavicle (89.9%), humerus (89.1%), and ulna (89.1%). This study provides sectioning points to easily estimate sex using Colombian population-specific formulae. This research aids in forensic individuation, as the long bones of the postcranial elements are relatively resistant to taphonomic processes. Furthermore, the ability to achieve such a high degree of success from a single bone is preferable for the fast-paced forensic anthropology laboratories in Colombia that process hundreds to thousands of cases each year. This research plays an important role in the development of population standards in Colombia and South America and provides a robust method that can withstand courtroom scrutiny.

•The reliability of osteometric data used in forensic case analyses is evaluated.•Twenty-two measurements had unacceptable technical error of measurement values.•A new manual, Data Collection Procedures 2.0, was produced based on these results.•The manual and an accompanying instructional video are freely available online. This study evaluates the reliability of osteometric data commonly used in forensic case analyses, with specific reference to the measurements in Data Collection Procedures 2.0 (DCP 2.0). Four observers took a set of 99 measurements four times on a sample of 50 skeletons (each measurement was taken 200 times by each observer). Two-way mixed ANOVAs and repeated measures ANOVAs with pairwise comparisons were used to examine interobserver (between-subjects) and intraobserver (within-subjects) variability. Relative technical error of measurement (TEM) was calculated for measurements with significant ANOVA results to examine the error among a single observer repeating a measurement multiple times (e.g. repeatability or intraobserver error), as well as the variability between multiple observers (interobserver error). Two general trends emerged from these analyses: (1) maximum lengths and breadths have the lowest error across the board (TEM<0.5), and (2) maximum and minimum diameters at midshaft are more reliable than their positionally-dependent counterparts (i.e. sagittal, vertical, transverse, dorso-volar). Therefore, maxima and minima are specified for all midshaft measurements in DCP 2.0. Twenty-two measurements were flagged for excessive variability (either interobserver, intraobserver, or both); 15 of these measurements were part of the standard set of measurements in Data Collection Procedures for Forensic Skeletal Material, 3rd edition. Each measurement was examined carefully to determine the likely source of the error (e.g. data input, instrumentation, observer’s method, or measurement definition). For several measurements (e.g. anterior sacral breadth, distal epiphyseal breadth of the tibia) only one observer differed significantly from the remaining observers, indicating a likely problem with the measurement definition as interpreted by that observer; these definitions were clarified in DCP 2.0 to eliminate this confusion. Other measurements were taken from landmarks that are difficult to locate consistently (e.g. pubis length, ischium length); these measurements were omitted from DCP 2.0. This manual is available for free download online (https://fac.utk.edu/wp-content/uploads/2016/03/DCP20_webversion.pdf), along with an accompanying instructional video (https://www.youtube.com/watch?v=BtkLFl3vim4).

•5 Craniometric landmarks were used to test accuracy across various experience levels.•No measurement accrued an 80% standard rate to be classified as “standardized in practice”.•This variation led to physical measurement differences between 2–48% PMD and up to 42mm.•We found that there is variation in literature and that training can also cause these differences.•We recommend that a unified text be created with a new single allowable tolerance for variation. Recently, forensic anthropology has undergone a major shift to quantitative methodologies, including the standardization of osteometric measurements. This study is an analysis of inter-observer error rates for eight “standardized” cranial measurements. Thirty participants, of varied experience, were asked to note the location of landmarks used in each measurement, and to record the resulting measurement. None of the tested measurements met the previously established 80% consensus rate necessary to be considered “standardized-in-practice” [1]. The highest consensus rate was that of nasal breadth (67.9%), followed by that of the mastoid height (57.1%). This low precision yielded percent mean differences of 2–48% of the total measurement, with range differences of up to 42mm. These results indicate that the field should take steps to improve osteometric standardization, including re-examining all measurements currently listed in osteometric canon and re-issuing a comprehensive guide.

This article demonstrates the utility of a multivariate analysis of vertebrae in an applied context. The human vertebral column is a morphologically complex group of elements. Current methods rely on morphological characteristics to classify isolated vertebrae qualitatively. This research provides a bridge between morphological assumptions for vertebral designations and quantitative classification. These osteometric methods and statistical analyses provide quantifiable information relating to the accuracy of vertebrae classification. The sample used for this analysis consists of osteometric vertebral measurements from intact vertebral columns from 59 individuals. In order to assess the potential for these vertebral measurements to classify vertebrae, regional grouping models based on vertebral column segments were developed and analyzed. The data were tested for multivariate normality and homogeneity of variance–covariance matrices in order to comply with the assumptions required by the statistical analyses used for classification. Linear discriminant function analysis was used for classification. The sensitivity and specificity of each vertebral group prediction were used for evaluation. This research demonstrates that by using osteometric methods and statistical analyses, the accuracy of vertebrae classification is quantifiable. This method has been developed to assist with the sorting and analysis of commingled and fragmentary skeletal remains.

The osteometric methods are the most reliable way to estimate the sex of skeletons when DNA analysis is not used. However, as osteometric studies usually ignore the overlap in male and female skeletal dimensions, they rarely achieve accuracy sufficient for forensic application. To resolve this issue, recent studies suggest sex estimation only when posterior probability (pp) is greater than 0.95, but that approach does not always provide sufficient accuracy and creates a large proportion of unsexed skeleton. Thus, our study aimed to explore whether it is possible to adjust pp on skeletal measurements with pronounced sexual dimorphism to meet 95% accuracy and to enable sex estimation on a reasonable proportion of individuals. From 207 skeletons, we included 65 postcranial measurements and selected 10% of variables with the highest sexual dimorphism. We computed univariate and bivariate discriminant functions using pp threshold of 0.5, 0.95, and the threshold required to achieve accuracy of ≥ 95%. Discriminant functions with pp=0.5 obtained accuracy of 85%–93%, while those with pp≥0.95 and adjusted posterior probabilities obtained 94%–99%. However, we showed that by selecting a particular threshold, sex could be estimated on a greater proportion of individuals than for pp≥0.95: 42%–86% vs. 24%–62% for univariate and 69%–95% vs. 49%–78% for bivariate functions. Therefore, when developing sex estimation models, we suggest not to use fixed pp level, but to adjust pp to achieve 95% accuracy and to minimize the percentage of unsexed skeletons.

•Eleven equations for sorting commingled upper limb bones are produced.•The regression equations can be applied in fragmented skeletal elements.•The method is applicable in skeletal samples of mixed sex and anatomical side.•A combined osteometric and morphoscopic approach can lead to a reliable sorting. The major upper limb skeletal elements (scapulae, humeri, ulnae and radii) are frequently utilized for sex determination and stature estimation. Consequently, in forensic cases that involve commingled remains, it is crucial to reassociate the aforementioned bones and attribute them to the right individual. The aim of the present study is to develop simple and multiple regression equations for sorting commingled human skeletal elements of the upper limb. In that context, ten common anthropological linear measurements of the articular surfaces of scapulae, humeri, ulnae, and radii were performed on 222 adult skeletons from the Athens Collection. The functions developed for sorting adjoining bones presented a strong positive linear relationship (r=0.69–0.93, p<0.05). The values of the determination coefficient statistics (r2=0.47–0.86) were found to be high and those of the standard errors of the estimate were found to be low (SEE=0.88–1.61). Blind tests indicated that when metric and morphoscopic sorting techniques are combined, a reliable sorting of the skeletal elements of the upper limbs is possible.

Applying dry bone osteometrics to virtual bone surfaces obtained via medical imaging raises the question of consistency between the variables. Variables obtained from virtual bone surfaces also need to be sufficiently repeatable and reproducible to be valid for anthropological studies. This is also true for the landmarks defining these variables and for their acquisition. The consistency between variables taken directly from dry bones and from the virtual surfaces of dry bones was tested on 40 clavicles. 30 virtual surfaces of iliae, fifth lumbar vertebrae, and clavicles reconstructed from computed tomography scans of living individuals were used to test the repeatability and reproducibility of 16 landmarks and 19 variables. Statistical tests, graphical and quantitative error evaluations, and intraclass correlation coefficients were applied. The differences between all variables taken on dry and virtual clavicles were less than ±1 mm. Bland–Altman plots showed more than 95% reliability between variables obtained on dry bone and their virtually reconstructed surfaces, confirming their consistency and thus validating their use in osteometric studies independently of the medium of study. Although not all landmarks were repeatable and reproducible, most variables were. To assess intra- or inter-observer errors, graphical representations or coefficients are more precise and accurate than statistical tests. These two evaluation methods should be given priority to test the repeatability and reproducibility of osteometric variables.