Karate injuries in children and adolescents

Abstract Objecti6es: To identify risk factors for injury and to establish safety guidelines for children in Uechi -Ryu karate. Design: A 1-year retrospective survey of injuries. Setting: A private karate school (Uechi -Ryu style) in Plymouth, MA. Patients: A total of 68 athletes (age 6-16 years; mean age 10 years) who participated in karate during the 1995 -1996 season. Inter6entions: None. Main outcome measures: The presence or absence of injury, with grading of injuries as major, moderate or minor. The types of injuries and body region involved were also analyzed. Results: Twenty eight percent of athletes sustained at least one injury. All injuries were minor, with no time off from training required. The injuries consisted primarily of bruises (11 of 19). Other injuries included mild sprains or strains (5 of 19) and having their 'wind knocked out' (3 of 19). Most injuries were localized to the extremities. Logistic regression analysis identified risk factors for injury. Risk of injury increased with number of years of training (odds ratio 2.95; 95% confidence interval 1.81-4.82; P B0.0001), number of hours per week (odds ratio 2.12; CI 1.15-4.21; P =0.016) and rank, specifically brown belt versus lower belts (odds ratio 6.56; CI 2.02 -21.26; P =0.006). Conclusions: Karate is a relatively safe sport for children and adolescents when properly taught. Risk of injury increases with experience; therefore, greater supervision is required of higher ranks. Injury increases with weekly training; however, 3 h a week or less appears to be associated with a low risk of significant injury in this age group

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis