Effects of the abdominal belt on muscle-generated spinal stability and L4/L5 joint compression force
Bibliographic record
Abstract
The goals of this study were (1) to determine the effects of abdominal belts on muscle-generated active lumbar spine stability, (2) to determine their effect on the subsequent joint compression force at L4/L5 and (3) to determine whether the effective stability of the spine could be predicted by the active spine stability and belt condition. Electromyographic (EMG) and trunk stiffness data from a previously reported experiment in which 10 subjects performed quick-release tasks (perturbation) with and without an abdominal belt were used as inputs to biomechanical models to estimate the active spine stability and effective stability of the spine, respectively. The subjects exerted isometric trunk flexion, extension and lateral bending trials at 0 and 80% of maximum intra-abdominal pressure when the resisted force was suddenly released. Wearing an abdominal belt had no significant effect on either the muscle-generated lumbar spine stability or the L4/L5 joint compression force in any direction. The effective stability of the spine was adequately predicted by the active spine stability and the effect of the belt, which accounted for approximately 34% of the effective spine stability. The study demonstrated that the abdominal belt contributed to the passive stability of the lumbar spine and did not change the active stability for tests performed within the same experimental session.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".