Impact of Personalized Mass Distribution on the Spinal Load Predictions of Rigid and Flexible Thorax Models during Forward Flexion
Bibliographic record
Abstract
Accurate modeling of musculoskeletal (MSK) dynamics tailored to individual patients is crucial for evaluating adult spinal deformities, especially in the thoracic region. This research explores the impact of personalized upper-body mass distribution on predictions of spinal loads using both rigid and flexible thorax models in neutral standing and forward flexion postures. Two MSK models were employed: a rigid thorax model with thoracic spinal discs treated as rigid joints, and a flexible thorax model with thoracic discs modeled as spherical joints allowing three degrees of freedom. These models incorporated constant percentage base (CPB) and body shape-based (BSB) mass distributions, resulting in four configurations: Rigid-CPB, Rigid-BSB, Flexible-CPB, and Flexible-BSB. In neutral standing, the root means square error (RMSE) averaged 16.96% BW in the anteroposterior direction and 24.79% BW in the proximodistal direction between the Rigid-CPB and Flexible-BSB models. These errors increased during forward flexion to 43.72% BW and 84.86% BW, respectively. In the mediolateral direction, RMSE was 6.69% BW during forward flexion and 0.02% BW during neutral standing. The normalized RMSE (nRMSE) averaged below 8.5% in both postures. This study underscores the effectiveness of the Flexible-BSB model in simulating intricate spinal deformities, offering valuable biomechanical insights. It emphasizes the critical role of personalized mass distribution and thoracic flexibility in improving the precision of MSK simulations for clinical applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".