The McGill Approach to Core Stabilization in the Treatment of Chronic Low Back Pain: A Review
Bibliographic record
Abstract
Abstract Introduction Low back pain (LBP) is a major cause of disability and is progressively becoming worse on a global scale. [1,2] The prevention and rehabilitation of LBP lacks clarity in part due to the heterogeneity of the exercise programs prescribed to treat low back pain. Some authors have proposed stabilizing exercises for lower back pain which exert minimal loads on the spine. [3,4,5] Despite a multitude of existing exercise therapies, McGill has introduced three exercises for rehabilitating lower back pain, termed the McGill Big Three (MGB3). [6,7,8,9,10] These include the curl-up, side plank and bird-dog. The purpose of this review is to investigate the clinical outcomes from prescribing the MGB3 to individuals with chronic LBP. Methods Inclusion criteria were randomized control trials that involved an intervention with MGB3 core stabilization exercises for patients with chronic low back pain. The research included articles published during any period in full English text. Studies were critically reviewed by two authors EL and GG independently and collaboratively. Results In total, four randomized control trials were included in this review. Multiple cohorts, with varying age, demographics and occupation were studied. Outcomes studied included various pain scores, patient reported functional and performance measures. Discussion Controlled clinical trials employing this method in low back pain treatment showed low quality data with mixed statistical significance, and little to no clinical significance irrespective of the measure used or even when compared to baseline. Limitations of these trials are detailed herein. Conclusion Currently there is limited data supporting the clinical benefit of the McGill approach for the treatment of low back pain based on the available randomized clinical trials. More study is required prior to widespread adoption into clinical practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".