The Effects of an 8‐Week Stabilization Exercise Program on Lumbar Multifidus Muscle Thickness and Activation as Measured With Ultrasound Imaging in Patients With Low Back Pain: An Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Lumbar stabilization exercise programs (LSEP) produce positive effects on clinical outcomes, but the underlying mechanisms remain relatively unexplored. Psychological and neuromuscular mechanisms can be involved, such as a better activation of the lumbar multifidus, which represents one possibility. OBJECTIVES: To determine the following: (1) the effect of an LSEP on lumbar multifidus muscle thickness and activation, as measured with rehabilitative ultrasound imaging (RUSI), in patients with low back pain (LBP); (2) the correlation between RUSI measures and any change in clinical outcomes following the LSEP; and (3) the reliability of RUSI measures in control subjects over 8 weeks. DESIGN: One-arm clinical trial with healthy subjects as a control group; reliability study. SETTING: LSEP delivered in a clinical setting; outcomes measured in a laboratory setting. PARTICIPANTS: A total of 34 patients with nonacute LBP and 28 healthy control subjects. METHODS: Outcomes were measured before and after an 8-week LSEP in patients with LBP, and at the same time interval (without treatment, to assess reliability) in control subjects. MAIN OUTCOME MEASUREMENTS: Pain numeric rating scale, Oswestry Disability Index (function), as well as RUSI measures for the lumbar multifidus (LM) muscles at 3 vertebral levels (L5-S1, L4-5, and L3-4) during rest (static) and dynamic contractions (percent thickness change). RESULTS: Patients did not show systematic changes in RUSI measures relative to controls, even though RUSI impairments were observed at baseline (dynamic measure at L5-S1) and even though patients had significant improvements in pain and disability. Correlational analyses with these clinical outcomes suggested that patients had reduced muscle thickness at baseline that was associated with a greater reduction in disability following LSEP; however, LM activation measured at baseline showed the opposite. Static RUSI measures showed excellent reliability at the L4-5 and L3-4 levels, whereas dynamic measures were not reliable. CONCLUSIONS: Patients showed less muscle activation than controls at baseline (L5-S1 level), but the LSEP did not normalize this impairment. The links between RUSI measures and the change in clinical outcomes during LSEP should be further explored. This clinical trial has been recorded in the International Standard Registered Clinical/soCial sTudy Number (ISRCTN) registry (ID: ISRCTN94152969). LEVEL OF EVIDENCE: II.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".