Evaluating stability of human spine in static tasks: a combined <i>in vivo</i>-computational study
Bibliographic record
Abstract
Various interpretations and parameters have been proposed to assess spinal stability such as antagonist muscle coactivity, trunk stiffness and spinal buckling load; however, the correlation between these parameters remains unknown. We evaluated spinal stability during different tasks while changing the external moment and load height and investigated likely relationships between different EMG- and model-based parameters (e.g., EMG coactivity ratio, trunk stiffness, force coactivity ratio) and stability margins. EMG and kinematics of 40 young healthy subjects were recorded during various quasi-static tasks. Muscle forces, trunk stiffness and stability margins were calculated by a nonlinear subject-specific EMG-assisted-optimization musculoskeletal model of the trunk. The load elevation and external moment increased muscle activities and trunk stiffness while all stability margins (i.e., buckling loads) decreased. The force coactivity ratio was strongly correlated with the hand-load stability margin (i.e., additional weight in hands to initiate instability; R2 = 0.54) demonstrating the stabilizing role of abdominal muscles. The total trunk stiffness (Pearson’s r = 0.96) and the sum of EMGs of back muscles (Pearson’s r = 0.65) contributed the most to the T1 stability margin (i.e., additional required load at T1 for instability/buckling). Force coactivity ratio and trunk stiffness can be used as alternative spinal stability metrics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".