The Active Straight Leg Raise Test and Lumbar Spine Stability
Bibliographic record
Abstract
OBJECTIVE: To determine the utility of the active straight leg raise (ASLR) test as a screen of lumbar spine stability and abdominal bracing (AB) ability. DESIGN: A biomechanical study of the ASLR test as a clinical evaluation of lumbar spine stability and AB. SETTING: Clinical research laboratory. PARTICIPANTS: Fourteen participants who were currently asymptomatic for back pain and leg pain were evaluated. METHODS: Spine posture, muscle activation, and pressure distributions underneath the supine subject were determined. MAIN OUTCOME MEASUREMENTS: An estimation of lumbar spine stiffness, a direct correlate with spine stability, was obtained using an anatomically detailed spine model. RESULTS: AB during the ASLR reduced the center of pressure (CoP) movement on a strain-based pressure mat in lumbar rotation (P < .0125) as well as reducing directly measured lumbar rotation (P = .02). Active AB increased lumbar spine stiffness (P < .002). Regression analysis between stiffness and CoP movement suggested that different participants used different strategies to control torso motion. CONCLUSIONS: This study demonstrates that the ASLR has utility as a screen of lumbar spine stability and AB ability. The ASLR maneuver can assess control of lumbar rotational movements in the transverse plane. Finally, this study demonstrated that AB can measurably improve the rotational (transverse plane) stiffness of the lumbar spine.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".