Rates and Patterns of Playing-Related Musculoskeletal Disorders in Drummers
Bibliographic record
Abstract
AIMS: Playing-related musculoskeletal disorders (PRMDs) are a significant health concern for percussionists. Although many of the known risk factors for PRMDs likely apply to all percussion subgroups (e.g., weekly practice hours, warm-ups/cool-downs, etc.), the rates and injury patterns in drummers (herein defined as 'percussionists who play the drum set') may differ due to differences in physical demands from those of other percussion subgroups. The goal of this study was to determine the drummer-specific rates and patterns of PRMDs. METHODS: An electronic survey including questions on respondent demographics, history and patterns of PRMDs, and potential drummer-specific risk factors for reporting PRMDs was distributed via social media using a snowball sampling technique. The target population included individuals aged 18 years or older who exclusively played the drum set (minimum 5 hrs/wk). The rates of PRMDs were analyzed by body region (e.g., upper/lower limb, etc.) and by location within body regions (e.g., shoulder, knee joint, etc.). RESULTS: The lifetime history of PRMDs in the study sample (n=831) was 68%, and 23% reported currently experiencing a PRMD. Most respondents reported multiple PRMDs (59%). The upper limb was the most commonly-affected body region (59%). The wrist joint (25%) and low back (24%) were the most commonly affected locations within body regions. CONCLUSIONS: Drummers' reporting of multiple PRMDs is consistent with previous findings in percussionists, but differences in the lifetime histories and patterns of injury supports the notion that risk factors may differ between percussion subgroups. Analysis of survey responses pertaining to drummer-specific risk factors is currently underway.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".