Abstract：
UNASSIGNED: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention.
UNASSIGNED: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting.
UNASSIGNED: Case-control study; Level of evidence, 3.
UNASSIGNED: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported \"immediately\" or \"at a delay\"), including both individual athlete/cadet and institutional characteristics.
UNASSIGNED: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers.
UNASSIGNED: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated.
UNASSIGNED: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting.
UNASSIGNED: Case-control study; Level of evidence, 3.
UNASSIGNED: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported \"immediately\" or \"at a delay\"), including both individual athlete/cadet and institutional characteristics.
UNASSIGNED: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers.
UNASSIGNED: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated.
摘要：
■脑震荡后的早期医疗护理可以最大程度地减少症状持续时间和负担；但是，许多脑震荡未确诊或受伤后诊断延迟。许多脑震荡症状（例如，头痛,头晕)不可见，这意味着早期识别通常取决于个人向医务人员报告他们的伤害。对脑震荡报告时解释因素的类型和水平的基本了解可以帮助确定有希望的干预方向。
■确定预测即时（与延迟）伤害报告的个人和机构因素。
■病例对照研究；证据水平，3.
■这项研究是对脑震荡评估数据的二次分析，研究与教育（CARE）联盟研究。样本包括3213名大学运动员和军校学员，他们在研究期间被诊断患有脑震荡。与会者来自美国的27个民事机构和3个军事机构。机器学习技术用于建立模型，预测谁会在脑震荡事件后立即报告受伤（由运动教练测量，表示受伤被报告为“立即”或“延迟”）。包括个人运动员/学员和机构特征。
■在整个样本中，结合各个因素，可以预测报告的即时性，平均准确度在55.8%到62.6%之间，取决于分类器类型和样本子集；添加机构因素将报告预测准确性提高了1到6个百分点。在个人层面,与损伤相关的精神状态改变和意识丧失对即时报告最具预测性，这可能是可观察到的迹象导致损伤报告被外部介导的结果。在机构层面,重要属性包括体育部门的年收入和运动员与运动教练的比例。
■需要进一步研究有关资源分配的机构决策的途径，包括关于运动医学人员配备的决定，可能有助于报告即时性。更广泛地说,所测试的机器学习模型的准确性相对较低，这表明继续扩展如何理解和促进报告的重要性。
■确定预测即时（与延迟）伤害报告的个人和机构因素。
■病例对照研究；证据水平，3.
■这项研究是对脑震荡评估数据的二次分析，研究与教育（CARE）联盟研究。样本包括3213名大学运动员和军校学员，他们在研究期间被诊断患有脑震荡。与会者来自美国的27个民事机构和3个军事机构。机器学习技术用于建立模型，预测谁会在脑震荡事件后立即报告受伤（由运动教练测量，表示受伤被报告为“立即”或“延迟”）。包括个人运动员/学员和机构特征。
■在整个样本中，结合各个因素，可以预测报告的即时性，平均准确度在55.8%到62.6%之间，取决于分类器类型和样本子集；添加机构因素将报告预测准确性提高了1到6个百分点。在个人层面,与损伤相关的精神状态改变和意识丧失对即时报告最具预测性，这可能是可观察到的迹象导致损伤报告被外部介导的结果。在机构层面,重要属性包括体育部门的年收入和运动员与运动教练的比例。
■需要进一步研究有关资源分配的机构决策的途径，包括关于运动医学人员配备的决定，可能有助于报告即时性。更广泛地说,所测试的机器学习模型的准确性相对较低，这表明继续扩展如何理解和促进报告的重要性。
