In-Line Skating Injuries
Bibliographic record
Abstract
BACKGROUND: The incidence of in-line skating injuries has increased with the rapid growth in the sport's popularity, but few studies have examined patterns of injuries.OBJECTIVE: This study sought to determine the demographics of at-risk in-line skaters, document protective equipment use, identify contributing or precipitating factors associated with injuries, and obtain a profile of the injuries sustained.DESIGN: This prospective, descriptive study examined injuries among in-line skaters seen in emergency departments from three hospitals in Canada from August 23, 1995, to November 19, 1996. Patients completed questionnaires about their injuries, and the data were used for the analysis.RESULTS: A total of 121 skaters completed the study. The mean age of injured skaters was 24 years. The largest age-group of injured skaters (50%) were those between 18 and 35 years old. Although the ratio of male to female skaters was about equal (45% female, 55% male), twice as many males as females were younger than 18. The most common cause of injury was a loss of control with no obstacles (50% of patients), and the most common injury was forearm fracture. Most injured skaters were either experienced recreational (33.2%) or novice (37.5%), and, when injured, most skaters were wearing less protective equipment than usual.CONCLUSION: Loss of control and inexperience were factors contributing to about half the injuries. Protective equipment among skaters was underused. Future research must identify the overall incidence of injuries, optimal design and efficacy of protective equipment, and effectiveness of preventive strategies such as safety education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".