On the importance of retaining stresses and strains in repositioning computational biomechanical models of the cervical spine
Bibliographic record
Abstract
Human body models are created in a specific posture and often repositioned and analyzed without retaining stresses that result from repositioning. For example, repositioning a human neck model within the physiological range of motion to a head-turned posture prior to an impact results in initial stresses within the tissues distracted from their neutral position. The aim of this study was to investigate the effect of repositioning on the subsequent kinetics, kinematics, and failure modes, of a lower cervical spine motion segment, to support future research at the full neck level. Repositioning was investigated for 3 modes (tension, flexion, and extension) and 3 load cases. The model was repositioned and loaded to failure in one continuous load history (case 1), or repositioned then restarted with retained stresses and loaded to failure (case 2). In case 3, the model was repositioned and then restarted in a stress-free state, representing current repositioning methods. Not retaining the repositioning stresses and strains resulted in different kinetics, kinematics, or failure modes, depending on the mode of loading. For the motion segment model, the differences were associated with the intervertebral disc fiber reorientation and load distribution, because the disc underwent the largest deformation during repositioning. This study demonstrated that repositioning led to altered response and tissue failure, which is critical for computational models intended to predict injury at the tissue level. It is recommended that stresses and strains be included and retained for subsequent analysis when repositioning a human computational neck model.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".