Abstract

Osteoarthritis (OA) is one of the most common pathologies encountered in dry bone contexts. However, even with the wealth of publications on documenting the presence of OA from skeletons, these studies prove to be largely incomparable due to different scoring methodologies and procedures in calculating prevalence. The standardization of a new OA data collection procedure would mitigate variability in evaluating, scoring, and calculating the prevalence of OA, thus allowing accurate comparison between studies. However, this level of data collection has often been described as unwieldy and lacking concordance. This research outlines a new methodology that utilizes Geographic Information Systems (GIS) to record OA characteristics, levels of expression, and spatial arrangement on the articular surfaces of the arm. The data was then processed using the analysis and visual rendering capabilities of GIS providing examples of OA patterning on the articular surface, within the joint, and within the individual. Using this method, large standardized OA datasets can be stored and the patterns within them modeled through the use of digitization, composite raster overlays, and modified binning techniques. The patterns recorded by this analysis can offer a more robust dataset on OA occurring within the arm that can provide the ability to explore OA progression and its relationship with biomechanical factors in larger datasets.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2019

Semester

Fall

Advisor

Schultz, John

Degree

Master of Arts (M.A.)

College

College of Sciences

Department

Anthropology

Degree Program

Anthropology

Format

application/pdf

Identifier

CFE0007789

URL

http://purl.fcla.edu/fcla/etd/CFE0007789

Language

English

Release Date

December 2020

Length of Campus-only Access

1 year

Access Status

Masters Thesis (Open Access)

Download

Share

COinS