Head Contact and Suspected Concussion Rates in University Basketball: Are Head Contact Penalties a Target for Prevention?
Bibliographic record
Abstract
OBJECTIVE: To compare head contact (HC) and suspected concussion incidence rates (IRs) in male and female university basketball players and describe associated game event and court location. DESIGN: Cross-sectional. SETTING: Canadian basketball courts. PARTICIPANTS: Players from 5 male and 5 female 2019 to 2020 regular season basketball games. ASSESSMENT OF RISK FACTORS: Prerecorded game footage was analyzed using Dartfish video analysis software to compare sexes. MAIN OUTCOME MEASURES: Poisson regression analysis was used to estimate IRs and incidence rate ratios (IRRs) for HCs and suspected concussions. Head contacts were classified as HC1 (direct, player-to-player) or HC2 (indirect, player-to-environment). Game event, court location, and penalization of HCs were reported. RESULTS: Two hundred thirty HCs (88.7% HC1s, 11.3% HC2s) were observed. The HC1 IR was higher in male than female players (IRR, 1.55; 95% CI, 1.16-2.06). Most HCs occurred within the key. Shooting was the primary offensive game event for male and female players for receiving HC1s (24.6% and 20.0%, respectively). Defensively, HC1s occurred most frequently while guarding an attacker for male players (40.6%) and rebounding for female players (31.0%). The suspected concussion IR was not significantly different between male and female players (IRR, 2.00; 95% CI, 0.20-19.8). In total, 11.2% of HC1s to defenders and 25.7% of HC1s to offensive players were assessed as a foul. CONCLUSION: Head contact rates were higher for male varsity basketball players compared with female players; however, suspected concussion rates did not differ. Game event and court locations differed by sex. A priority target for injury prevention is penalization of HCs because most HCs in competition went unpenalized.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".