Bibliographic record
Abstract
Re: S Thakore, J Tram, BE Hagel, T Kyle, T Senger, F Belanger. Injuries among wheeled shoe users: A comparison with other nonmotorized wheeled activities. Paediatr Child Health 2009;14(8):509–513. To the Editor; I would like to share with your readers the results of a Canadian Hospitals Injury Reporting and Prevention Program database analysis that were reported at a Canadian Society of Epidemiology and Biostatistics conference in 2003. A total of 3051 records from April 1990 to June 2002 from the Nova Scotia Canadian Hospitals Injury Reporting and Prevention Program database involving nonmotorized wheeled sports were analyzed. χ2 tests were performed for three pairwise comparisons among injured cyclists, skate-boarders and in-line skaters. Scooters were not included because the demographic was younger and significantly fewer records were available, and ‘Heelys’ had not yet made their debut in Nova Scotia. Of note, helmet legislation for cycling was introduced in Nova Scotia in 1997, while helmet legislation for all wheeled sports was introduced in 2003 after this study was completed. The sample included 71% male subjects (average age 10.3 years [12.8 years for skateboarding and 11.6 years for in-line skating]); 78.9% of the sample were injured while cycling, 12.6% while skateboarding and the remaining 8.5% while in-line skating. The admission rate was equal across all age groups at 10% and included a predominance of admissions related to fractures. Fractures comprised 32% of the injuries overall. Fractures were more common in in-line skating (53%) and skate-boarding (35%) than in cycling (26%) (P<0.02); however, more cyclists were admitted with their fractures than in-line skaters (26% versus 16%; P=0.02), with skateboarders (24%) being similar to cyclists. This would suggest that the fractures sustained in in-line skating could be of a less severe nature. Two per cent of in-line skaters wore protective gear other than helmets, while 1% of skateboarders and fewer than 0.1% of cyclists reported the use of additional protective gear. Head injuries (n=200) consisted of 7% of total injuries. Of these head injuries, 184 (92%) were related to cycling. When potentially confounding factors were included in a logistic regression model, the risk of head injury from skate-boarding had an adjusted OR of 0.24 (95% CI 0.12 to 0.47) and the risk from in-line skating had an adjusted OR of 0.34 (95% CI 0.16 to 0.74) relative to cycling. Wearing a helmet gave an adjusted OR of 0.43 (95% CI 0.31 to 0.61) for head injury across all sports. Looking at prehelmet legislation (1990 to 1996) versus posthelmet legislation (1997 to 2002), there was no significant decrease in the proportion of cyclists presenting with head injury (8.2% pre versus 7.1% post; P=0.28). Nevertheless, helmet use increased from 20% overall to 54% overall, and specifically from 21% to 63% in cycling, 1% to 22% in in-line skating and 0% to 46% in skateboarding. Compared with prehelmet legislation, the posthelmet legislation group had an OR of 4.6 (95% CI 3.9 to 5.4) for wearing a helmet. The incidence of cycling injuries in absolute numbers per year remained stable (range of 169 to 235 injuries per year), while skateboarding injuries increased (mean 21 injuries per year in the first five years to 44 injuries per year in last five years), reflecting an increase in participation rates. In summary, legislation was effective in increasing helmet use and reducing the risk of a head injury, but not in reducing the absolute proportion of head injuries in cyclists. In-line skaters should wear protective gear, in particular wrist guards, to prevent fractures.
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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 | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.097 | 0.045 |
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".