Factors associated with injuries occurred on slope intersections and in snow parks compared to on-slope injuries

Ruedl, Gerhard; Kopp, Martin; Sommersacher, R.; Woldrich, Tomas; Burtscher, Martin

doi:10.1016/j.aap.2012.09.019

Cited by 54 publications

(55 citation statements)

References 17 publications

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“…Second, although our hospital is located less then 6 km from SnowWorld, patients could have been brought to other hospital. Furthermore, several risk factors, i.e., experience level [19], alcohol consumption [20], place of accident on the slope, e.g., fun parks (recreation areas containing terrain to perform tricks) [18,21] and skill level [22] were not taken into account.…”

Section: Discussionmentioning

confidence: 99%

Annual trauma load of the world’s largest indoor skiing center

Laarhoven

Latten

Loos

et al. 2016

Eur J Trauma Emerg Surg

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Section: Discussionmentioning

confidence: 99%

Annual trauma load of the world’s largest indoor skiing center

Laarhoven

Latten

Loos

et al. 2016

Eur J Trauma Emerg Surg

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“…Weekly ski passes were analyzed, as injury records for this subgroup were available. Injury occurrence measures are: injury per thousand skier-days IPTSD = 1.46, and mean days between injury MDBI = 686.73, which is similar to other ski resorts [17]. Ski injuries are rare events usually occurring at small rates (less than 0.2 %).…”

Section: From the Ski Patrol Injury Recordsmentioning

confidence: 93%

“…Various risk factors have been reported, such as: gender [17], age [17], personality types [3], skier collision [8], skiing errors [5], speed of skiing [5,8], fatigue [5], perception of low difficulty [5], skillfulness and experience [8], quality of equipment [8], quality of ski slopes and quality of preparation, collision against objects, and jumps [8].…”

Section: Background In Ski Injury Researchmentioning

confidence: 99%

Identifying High-Number-Cluster Structures in RFID Ski Lift Gates Entrance Data

Delibašić

Obradović

2015

Ann. Data. Sci.

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In this paper we identify skier groups in data from RFID ski lift gates entrances. The ski lift gates' entrances are real-life data covering a 5-year period from the largest Serbian skiing resort with a 32,000 skier per hour ski lift capacity. We utilize three representative algorithms from three most widely used clustering algorithm families (representative-based, hierarchical, and density based) and produce 40 algorithm settings for clustering skiing groups. Ski pass sales data was used to validate the produced clustering models. It was assumed that persons who bought ski tickets together are more likely to ski together. AMI and ARI clustering validation measures are reported for each model. In addition, the applicability of the proposed models was evaluated for ski injury prevention. Each clustering model was tested on whether skiing in groups increases risk of injury. Hierarchical clustering algorithms showed to be very efficient in terms of finding the high-number-cluster structure (skiing groups) and for detecting models suitable for injury prevention. Most of the tested clustering algorithms models supported the hypothesis that skiing in groups increases risk of injury.

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“…Während das generelle Risiko für Kopfverletzungen durch das Tragen von Helmen sinkt, hat eine retrospektive Studie 2015 gezeigt, dass sich die Anzahl der schweren Kopfverletzungen dadurch nicht beeinflussen lässt [1]. Aufgrund der generellen Reduktion von Kopfverletzungen [7,9] wird das Tragen von Skihelmen als Verletzungspräventi-on weiterhin empfohlen [8]. He who has a head, protects it.…”

Section: Literaturdaten Zum Tragen Von Helmen Beim Wintersportunclassified