Determining the Stabilizing Role of Individual Torso Muscles During Rehabilitation Exercises
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".