Cross-sectional study of characteristics and prevalence of musculoskeletal complaints in 1170 male golfers
Bibliographic record
Abstract
Objectives The primary aim was to describe the characteristics and prevalence of musculoskeletal complaints of a large group of non-professional golfers. Secondary aims were to compare golfers different in (A) skill-level, (B) presence of low back pain (LBP) and (C) performance of prevention exercises. Methods A sample of 1170 male golfers (mean age 54.98, SD=13.3) were surveyed online on personal and golf-specific characteristics, medical history and complaints in the preceding 7 days. Subgroups (A) with different golfing handicap (0 to 5, >5 to 10, >10), (B) with and without LBP and (C) who performed versus did not perform injury prevention exercises were compared using analysis of variance and χ 2 test. Results The prevalence and severity of musculoskeletal complaints was similar in everyday life and when playing golf. More than one-third of the golfers (n=436; 37.3%) reported LBP in the preceding 7 days, while other frequently affected body parts were the shoulder and knee. Golfers with different skill level differed in age and most golf-related characteristics but not in prevalence and severity of musculoskeletal complaints. Golfers with and without LBP were similar in almost all variables. Golfers who performed prevention exercises (n=371; 27.1%) were older and had a higher prevalence of complaints. Conclusion The prevalence and severity of musculoskeletal complaints in golfers were similar to the wider population. It seems that injury prevention exercises were implemented after injury, rather than as primary prevention. Prospective studies looking at the epidemiology of injury, risk factors and interventions are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".