引导式测验 · 中等 等级

运动员受伤预测测验中的人工智能

审查工作量特征、基于 GPS 的伤害研究、类别不平衡、验证限制、模型指标以及伤害风险估计的不确定性。

相关引导路径人工智能在运动员损伤预测中的应用
问题 1 的 8

急慢性工作负荷比总结了什么？

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  1. 急慢性工作负荷比总结了什么？
    • A recent workload relative to a longer-term workload summary
    • A player’s injury severity relative to salary
    • A team’s travel time relative to match time
    • Heart-rate variability relative to sleep duration
  2. 当结果不常见时，为什么损伤模型难以评估？
    • Every athlete has the same outcome
    • A model can appear accurate while missing many rare injury events
    • Rare events eliminate the need for validation
    • A high accuracy score guarantees useful prediction
  3. 澳大利亚足球工作负荷研究使用了哪些测量指标？
    • Ticket scans and social-media posts
    • Only heart-rate variability
    • GPS, accelerometer, and player-perceived exertion data
    • Only video of match officials
  4. 损伤预测模型估算的是什么？
    • A guaranteed date of injury
    • A clinical diagnosis without examination
    • A certain training prescription
    • A probability or risk estimate for a defined outcome and period
  5. Carey等人对后续赛季的非接触性损伤预测报告了什么？
    • Performance was only marginally better than chance, with AUC below 0.65
    • Every outcome had perfect accuracy
    • AUC was always 1.0
    • The study did not test a later season
  6. What validation gap did the 2026 systematic review report for all included studies?
    • All used a separate country for testing
    • None used external validation
    • All models were tested prospectively in clinics
    • No study reported any outcome metrics
  7. Which split did the Australian football study use to test its injury forecasts?
    • It mixed all three seasons randomly for every result
    • It trained only on injury labels from the third season
    • It trained using the first two seasons and evaluated predictions on the third
    • It evaluated only on the training rows
  8. What do sensitivity and specificity measure in an injury model?
    • They both measure only training speed
    • Sensitivity is cost and specificity is model size
    • They are alternative names for data leakage
    • Sensitivity concerns detecting injured cases; specificity concerns correctly identifying non-injured cases