Comparison of spine motion in elite golfers with and without low back pain
Bibliographic record
Abstract
Low back pain is a common musculoskeletal disorder affecting golfers, yet little is known of the specific mechanisms responsible for this injury. The aim of this study was to compare golf swing spinal motion in three movement planes between six male professional golfers with low back pain (age 29.2+/-6.4 years; height 1.79+/-0.04 m; body mass 78.2+/-12.2 kg; mean +/- s) and six without low back pain (age 32.7+/-4.8 years; height 1.75+/-0.03 m; body mass 85.8+/-10.9 kg) using a lightweight triaxial electrogoniometer. We found that golfers with low back pain tended to flex their spines more when addressing the ball and used significantly greater left side bending on the backswing. Golfers with low back pain also had less trunk rotation(obtained from a neutral posture), which resulted in a relative 'supramaximal' rotation of their spines when swinging. Pain-free golfers demonstrated over twice as much trunk flexion velocity on the downswing, which could relate to increased abdominal muscle activity in this group. This study is the first to show distinct differences in the swing mechanics between golfers with and without low back pain and provides valuable guidance for clinicians and teachers to improve technique to facilitate recovery from golf-related 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".