Golf-Related Low Back Pain: A Review of Causative Factors and Prevention Strategies
Bibliographic record
Abstract
Golf is a popular sport with both perceived and real health benefits. However, certain injury risks are also prevalent, particularly to the lower back. Epidemiological studies have shown that lower back pain (LBP) from golf account for between 18% and 54% of all documented ailments, leading many researchers to regard the condition as the most common golf injury. The purpose of this review was to examine the scientific literature to ascertain the risk factors associated with the development of LBP from playing golf and suggest methods to modify or limit these factors. Results of the review indicate that the high frequency of LBP appears multi-factorial although the asymmetrical and forceful nature of the swing along with excessive play and practice, particularly amongst elite players, appear to be common factors. Other factors include swing flaws leading to excessive side-bend and over-rotation of the spine, abnormal muscle recruitment, poor trunk endurance, restricted lead hip internal rotation and the use of unnecessarily stressful club transportation methods. Methods to help control or eliminate excessive stress on the lower back would include reducing the amount spent playing or practicing, seeking professional assistance to assess and adjust swing mechanics, improve trunk and hip flexibility, increase the strength and endurance of the trunk musculature, consider different footwear options and avoid carrying the golf bag. Adopting some or all of these recommendations should allow players to continue to enjoy the sport of golf well into their senior years.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".