Prevalence of Medical Problems Associated with Playing the Great Highland Bagpipe: Survey Results and Comparisons to Other Musicians
Bibliographic record
Abstract
The number of players of the Great Highland bagpipes (GHB) involved in competition worldwide is unknown. Despite the rising popularity of piping and pipe band organizations, minimal information is available regarding the range of medical problems encountered in pipers. The purpose of this study was to describe the neuromusculoskeletal problems experienced by bagpipers. A survey adapted from the National Flute Association Medical Problems Survey was used and distributed to pipers in the United States and Canada (n = 123). The demographic profile showed that 31% of the respondents have played the GHB for 3 to 8 yrs and 29% have played 20+ yrs. On average, pipers practiced 5 to 15 hrs/wk, and the most common sites of musculoskeletal complaint were the left arm and lower back (32% each). Loss of finger coordination (21%) and neck pain or stiffness (19%) were the next most common complaints. Pain and stiffness were also reported in the left (17%) and right (15%) hand and the left shoulder (11%). The survey results support the concerns expressed by pipers regarding problems resulting from playing the GHB and find these issues correlate with those described in other musician populations. Having determined the primary areas of concern, identifying possible biomechanical and ergonomic issues, as well as instrument-specific strengthening techniques may decrease rates of neuromusculoskeletal problems in the piping population.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".