Chronic Low Back Pain: A Critical Review of Specific Therapeutic Exercise Protocols on Musculoskeletal and Neuromuscular Parameters
Bibliographic record
Abstract
Although exercise is often used in the treatment of chronic non-specific low back pain, little is known about the efficacy of specific exercises on the physiological or structural processes underlying this form of back pain. We evaluated the current published studies that used specific exercise interventions for non-specific low back pain and that utilized strength, endurance, neuromuscular control, flexibility, or posture as primary outcome variables. Our review revealed that 11 different control trials fit our criteria (15 published papers), with the majority evaluating strengthening protocols (N=13). Moderate evidence indicates that specific exercises improve abdominal and trunk extensor strength and endurance, while minimal evidence supports improvements in neuromuscular control characteristics, posture, spinal motion, or muscle tissue characteristics. Most studies reported improvements in both functional daily activities as well as an accompanying reduction in low back pain. We concluded that more thorough investigations utilizing better diagnostic classifications are needed to determine whether specific exercise protocols produce the desired effects on neuromuscular control impairments as well as on the mechanical environments that have been shown to contribute detrimentally to low back pain.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".