Core Stability Exercises for Low Back Pain in Athletes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to systematically review the evidence for the effectiveness of core stability exercises for treating athletes with low back pain (LBP). DATA SOURCES: We searched several databases (Medline, AMED, CINAHL, SportDiscus, and EMBASE). Our eligibility criteria consisted of articles published in a peer-reviewed journal in English, using any prospective clinical study design, where athletes with nonspecific LBP were treated with core stability exercises in at least 1 study arm, and back pain intensity and/or disability were used as outcome measures. All included randomized controlled trials (RCTs) were assessed for risk of bias using the Cochrane Risk of Bias tool, whereas non-RCT studies were assessed for quality using the Downs and Black checklist. MAIN RESULTS: Five studies including 151 participants met the inclusion criteria, including 2 RCTs. The quality of the literature on this topic was deemed to be low overall, with only 1 non-RCT having a moderate quality score, and 1 RCT having a lower risk of bias. Four studies reported statistically significant decreases in back pain intensity in their core stability intervention group. CONCLUSIONS: The quantity and quality of literature on the use of core stability exercises for treating LBP in athletes is low. The existing evidence has been conducted on small and heterogeneous study populations using interventions that vary drastically with only mixed results and short-term follow-up. This precludes the formulation of strong conclusions, and additional high quality research is clearly needed.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".