Treating low back pain in athletes: a systematic review with meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To summarise the evidence for non-pharmacological management of low back pain (LBP) in athletes, a common problem in sport that can negatively impact performance and contribute to early retirement. DATA SOURCES: Five databases (EMBASE, Medline, CINAHL, Web of Science, Scopus) were searched from inception to September 2020. The main outcomes of interest were pain, disability and return to sport (RTS). RESULTS: Among 1629 references, 14 randomised controlled trials (RCTs) involving 541 athletes were included. The trials had biases across multiple domains including performance, attrition and reporting. Treatments included exercise, biomechanical modifications and manual therapy. There were no trials evaluating the efficacy of surgery or injections. Exercise was the most frequently investigated treatment; no RTS data were reported for any exercise intervention. There was a reduction in pain and disability reported after all treatments. CONCLUSIONS: While several treatments for LBP in athletes improved pain and function, it was unclear what the most effective treatments were, and for whom. Exercise approaches generally reduced pain and improved function in athletes with LBP, but the effect on RTS is unknown. No conclusions regarding the value of manual therapy (massage, spinal manipulation) or biomechanical modifications alone could be drawn because of insufficient evidence. High-quality RCTs are urgently needed to determine the effect of commonly used interventions in treating LBP in athletes.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.025 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".