Characteristics of Head and Neck Injuries Among Pediatric Ice Skaters in the United States
Bibliographic record
Abstract
Purpose Injuries sustained while ice skating are common; however, the prevalence, distribution, and anatomical location of head and neck injuries (HNI) are not well known. The purpose of this study was to describe patterns in ice-skating-related head and neck injuries in children. Study design This was a retrospective review. Methods We conducted a retrospective review of data involving children aged 18 years and younger from the National Electronic Injury Surveillance System, a public database containing information from approximately 100 emergency departments across the United States. We utilized data from January 2002 to December 2021. Data gathered included patient demographics, injury type, injury location, injury outcome, and year of incidence. Results A total of 2686 ice-skating injuries were identified. 1594 (59%) of those involved the face, neck, ear, chin, nose, forehead, or mandible. Of these, 1023/1594 (64%) occurred in males and 571/1594 (36%) in females; 1653 (62%) patients sustained a laceration. The most commonly affected site of HNI was the chin, with 911/1594 (57%) injuries. Injuries to the neck were most commonly a sprain/strain with 32 injuries, while those affecting the mandible were most commonly a fracture with 4 injuries. Conclusion Young ice-skaters are most likely to sustain lacerations or other injuries to the chin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".