Biomechanics of a Physically Impaired Individual from Early Medieval Ranelagh, Ireland
Bibliographic record
Abstract
This research describes the methods used to re-create the biomechanics of a physically impaired middle-aged male, SK67, dated 893–1023 C.E., from the early medieval cemetery at Ranelagh, Co. Roscommon, Ireland, who experienced an antemortem femoral head fracture. A gap in research exists between the osteological evidence and the nature of the true biomechanical gait. Computer simulation of musculoskeletal models is an emerging engineering approach to estimate true gait patterns from geometric measurements of skeletal anatomy. This study paves the way for bridging the gap between osteological evidence and true biomechanics by obtaining information from SK67 and using it to build a three-dimensional musculoskeletal model of SK67 using OpenSim. Gait was then simulated using SCONE software (a musculoskeletal simulation optimization system to predict task-specificmotion), thereby revealing SK67’s likely limb function post-impairment. The patterns of habitual biomechanical stress are reflected in the quantity and distribution of cortical bone; based on this approach, the individual’s limbs were compared for signs of weight-bearing by analyzing cortical bone thickness from radiographs. The upper limbs were also examined for entheseal changes that might indicate the use of walking aids. The results of these interdisciplinary methods enabled the biomechanics of SK67 to be revealed and assessed how SK67 would have used walking aids to regain mobility and independence in the wake of their major trauma. The lack of arms on the musculoskeletal models may provide a limitation to this study, as well as the errors/learning curve in altering inputfiles to simulate the gait. This method opens new avenues for future research into reconstructing the biomechanics of individuals with physical impairments within archaeological contexts, across both time and location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".