Lumbar stabilisation exercises versus back endurance-resistance exercise training in athletes with chronic low back pain: protocol of a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain (CLBP) is an important disorder in athletes that may negatively affect their performance in competitions. The literature usually recommends physiotherapy based on exercises for back pain management in athletes. Recent evidence suggests that interventions based on lumbar muscle stabilisation exercises (LMSE) and back endurance-resistance exercises (BERE) may improve back pain and function performance. However, it is still unclear which type of exercise is more effective for the treatment of CLBP in athletes. OBJECTIVE: To compare the efficacy of LMSE versus BERE in athletes with CLBP. DESIGN: The study is a 2-arm, prospectively registered, randomised controlled trial. SETTING: The physical therapy clinical and biomechanics laboratory of the UNOPAR University. PARTICIPANTS: 32 male athletes with CLBP, age between 18 and 40 years old, recruited from the local community. INTERVENTION: An 8-week intervention programme will be carried out with LMSE s versus BERE. MEASUREMENTS: Trunk neuromuscular patterns during balance tasks (unipodal and over a ball) using electromyography and force platform parameters, pain, disability, fear and avoidance will be assessed by a blinded assessor at baseline and at follow-up after 8 weeks of intervention period. LIMITATIONS: The absence of blinding intervention and the exclusion of female athletes, seated sports and swimmers will affect the internal and external validity of the study. CONCLUSIONS: The results of this study will elucidate which of these two interventions promote better results in trunk neuromuscular pattern, back pain and function in male athletes with CLBP.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".