Musculoskeletal Injuries in Elite Able-Bodied and Wheelchair Foil Fencers—A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: To explore the incidence of musculoskeletal injuries in elite able-bodied and wheelchair foil fencers. DESIGN: A 3-year prospective cohort study of sport injuries during 2006-2009. SETTING: A sample of elite able-bodied fencers (AFs) and wheelchair fencers (WFs) from the Hong Kong National Squad. PARTICIPANTS: A total of 14 wheelchair and 10 able-bodied elite fencers completed the 3-year study. METHODS: Monthly interviews with fencers to collect data related to their injuries. MAIN OUTCOME MEASURES: The incidence rate and relative risk of injury were analyzed among able-bodied and WFs with different trunk control ability. RESULTS: Wheelchair fencers had higher overall injury incidence rate (3.9/1000 hours) than AFs (2.4/1000 hours). Wheelchair fencers with poor trunk control were more vulnerable to injuries (4.9/1000 hours) than those with good trunk control (3.0/1000 hours). Upper extremity injuries were predominant in WFs (73.8%), with elbow (32.6%) and shoulder strain (15.8%) being the most common injuries. Lower extremity injuries were predominant in AFs (69.4%), with muscle strain over knee and thigh region (22.6%), ankle sprain (14.5%), and knee sprain (11.3%) being the leading injuries. CONCLUSIONS: Results of this pilot study highlighted the distinct injury incidence between the 2 different fencer groups. Larg-scale epidemiologic and biomechanical studies are warranted to improve the understanding of fencing injuries to develop specific injury prevention/rehabilitation programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".