Effects of Using Generic vs. Subject-Specific Muscle Properties on Spinal Load Prediction Across Different Posture Simulations
Bibliographic record
Abstract
Subject-specific musculoskeletal models hold promise for adult spinal deformity management. However, fully subject-specific models require subject-specific soft-tissue properties not typically available in clinical settings. Models created using generic properties are more accessible but potentially less accurate. The objective of this study was to identify which biomechanical properties of muscle function, and in which specific body positions, exhibit significant differences when implementing generic versus subject-specific properties. Using OpenSim, we analyzed 250 subject-specific models, focusing on four muscle parameters: geometry-path, maximum-isometric-force, optimal-fiber-length, and tendon-slack-length across 11 postures, encompassing standing and flexed postures. A linear mixed-effects model evaluated the impact of muscle parameters on spinal compression loads. Differences in compression load between the models with subject-specific and generic data were compared statistically using non-parametric methods. Subject-specific geometry-path and maximum-isometric-force significantly influenced spinal compression loads, with mean differences of 13 % and 8 %, respectively. Differences were posture-dependent (geometry-path p < 0.001; max-isometric-force p = 0.005). Optimal-fiber-length (p = 0.053) and tendon-slack-length (p = 0.680) showed minimal impact (∼1% difference). Flexed postures were more sensitive to generic muscle parameters, with mean differences of 17 % (geometry-path) and 6 % (max-isometric-force), compared to standing (6 % and 4 %, respectively). The pronounced deviations observed in flexion simulations emphasized the necessity of subject-specific data in such simulations. However, when subject-specific data is not available, simulations based on standing postures are the least affected by the use of generic properties.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".