Head Contact and Suspected Concussion Rates in Youth Basketball: Time to Target Head Contact Penalties for Prevention
Bibliographic record
Abstract
OBJECTIVE: To compare game events, head contact (HC) rates, and suspected concussion incidence rates (IRs) in boys' and girls' youth basketball. DESIGN: Cross-sectional. SETTING: Canadian club basketball teams (U16-U18). PARTICIPANTS: Players from 24 boys' and 24 girls' Canadian club basketball teams during the 2022 season. ASSESSMENT OF RISK FACTORS: Recorded games were analyzed using Dartfish video analysis software to compare sexes. MAIN OUTCOME MEASURES: Poisson regression analyses were used to estimate HCs [direct (HC1) and indirect (HC2)], suspected concussion IRs, and IR ratios (IRRs). Game event, court location, and HC1 fouls were reported. RESULTS: Division 1 HC rates did not differ between boys (n = 238; IR = 0.50/10 player-minutes; 95% confidence interval [CI], 0.43-0.56) and girls (n = 220; IR = 0.46/10 player-minutes; 95% CI, 0.40-0.52). Division 2 boys experienced 252 HCs (IR = 0.53/10 player-minutes; 95% CI, 0.46-0.59); girls experienced 192 HCs (IR = 0.40/10 player-minutes; 95% CI, 0.35-0.46). Division 2 boys sustained higher HC1 IRs compared with Division 2 girls (IRR = 1.42; 95% CI, 1.15-1.74). Head contacts, rates did not differ between boys and girls in either Division. Suspected concussion IRs were not significantly different for boys and girls in each Division. Head contacts occurred mostly in the key for boys and girls in each Division. Despite illegality, HC1 penalization ranged from 3.9% to 19.7%. Head contact mechanisms varied across Divisions and sexes. CONCLUSIONS: Despite current safety measures, both HCs and suspected concussions occur in boys' and girls' basketball. Despite the illegality and potential danger associated with HC, only a small proportion of direct HCs were penalized and therefore targeting greater enforcement of these contacts may be a promising prevention target.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".