Neck injuries presenting to emergency departments in the United States from 1990 to 1999 for ice hockey, soccer, and American football
Bibliographic record
Abstract
OBJECTIVE: To examine the number and rate of neck injuries in the community as a whole for ice hockey, soccer, and American football by analysing data from patients presenting to emergency departments in the United States from 1990 to 1999. METHODS: Data compiled for the US Consumer Product Safety Commission were used to generate estimates for the total number of neck injuries and the more specific diagnoses of neck fractures, dislocations, contusions, sprains, strains, and lacerations occurring nationally from 1990 to 1999. These data were combined with yearly participation figures to generate rates of injury presenting to emergency departments for each sport. RESULTS: There were an estimated 5038 neck injuries from ice hockey, 19,341 from soccer, and 114 706 from American football. These could be broken down as follows: 4964 contusions, sprains, or strains from ice hockey, 17,927 from soccer, and 104 483 from football; 105 neck fractures or dislocations from ice hockey, 214 from soccer, and 1588 from football; 199 neck lacerations for ice hockey, 0 for soccer, and 621 for football. The rates for total neck injuries and combined neck contusions, sprains, or strains were higher for football than for ice hockey or soccer in all years for which data were available. CONCLUSION: The rate of neck injury in the United States was higher in football than in ice hockey or soccer in the time period studied.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".