Determining the Stabilizing Role of Individual Torso Muscles During Rehabilitation Exercises
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Bibliographic record
Abstract
In Brief Study Design. A systematic biomechanical analysis involving an artificial perturbation applied to individual lumbar muscles in order to assess their potential stabilizing role. Objectives. To identify which torso muscles stabilize the spine during different loading conditions and to identify possible mechanisms of function. Summary of Background Data. Stabilization exercises are thought to train muscle patterns that ensure spine stability; however, little quantification and no consensus exists as to which muscles contribute to stability. Methods. Spine kinematics, external forces, and 14 channels of torso electromyography were recorded for seven stabilization exercises in order to capture the individual motor control strategies adopted by different people. Data were input into a detailed model of the lumbar spine to quantify spine joint forces and stability. The EMG signal for a particular muscle was replaced either unilaterally or bilaterally by a sinusoid, and the resultant change in the stability index was quantified. Results. A direction-dependent-stabilizing role was noticed in the larger, multisegmental muscles, whereas a specific subtle efficiency to generate stability was observed for the smaller, intersegmental spinal muscles. Conclusions. No single muscle dominated in the enhancement of spine stability, and their individual roles were continuously changing across tasks. Clinically, if the goal is to train for stability, enhancing motor patterns that incorporate many muscles rather than targeting just a few is justifiable. The stabilizing role of individual lumbar muscles was quantified. No single muscle dominated in the enhancement of spine stability; furthermore, their individual roles continuously changed across tasks. Clinically, if the goal is to train for stability, enhancing motor patterns that incorporate many muscles rather than targeting a few is justifiable.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it