Quantifying Tissue Loads and Spine Stability While Performing Commonly Prescribed Low Back Stabilization Exercises
Bibliographic record
Abstract
In Brief Study Design. A quantitative biomechanical comparison of seven different lumbar spine “stabilization exercises.” Objectives. The purpose of this research was to quantify lumbar spine stability resulting from the muscle activation patterns measured when performing selected stabilization exercises. Summary of Background Data. Many exercises are termed “stabilization exercises” for the low back; however, limited attempts have been made to quantify spine stability and the resultant tissue loading. Ranking resultant stability together with spinal load is very helpful for guiding clinical decision-making and therapeutic exercise design. Methods. Eight stabilization exercises were quantified in this study. Spine kinematics, external forces, and 14 channels of torso EMG were recorded for each exercise. These data were input into a modified version of a lumbar spine model described by Cholewicki and McGill (1996) to quantify stability and L4–L5 compression. Results. A rank order of the various exercises was produced based on stability, muscle activation levels, and lumbar compression. Conclusions. Quantification of the calibrated muscle activation levels together with low back compression and resultant stability assists clinical decisions regarding the most appropriate exercise for specific patients and specific objectives. Lumbar spine stability was quantified during different stabilization exercises. Spine kinematics, external forces, and torso EMG were input into various lumbar spine models to quantify spine stability and L4–L5 compression. A rank order was produced of the various exercises based on stability, muscle activation, and L4–L5 compression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".