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Validationofaclassallocationmethodforwheelchairtrackathleteswithimpairedstrength– aproofofconceptstudy
MarkConnick,EmmaBeckman,SeanTweedy
TheUniversityofQueensland,Brisbane,AustraliaPhotobySimonBruty
Von Winterfeldt (2013). Proc. Natl. Acad. Sci. U.S.A. 2013 Aug 20; 110(Suppl 3): 14055–14061
EVIDENCE‐BASEDDECISIONMAKING
• Similaritieswithevidence‐basedmedicine?
• Centraltenets?
• Howshouldweproceed?
SCIENTIFICBASIS?
DECISION‐MAKINGINTHECURRENTSYSTEM
BASEDONCLINICALREASONING
Performance
EVIDENCE‐BASEDSYSTEM
TRANSPARENT,VALIDATEDDECISIONS
IDENTIFICATIONOFIMPAIRMENTTESTS
Isometricstrengthmeasure
PerformanceOutcome
Top‐Speed(0‐15m)correlation
Top‐Speed(Absolute)correlation
StrongestForearmPronation 0.70* 0.79*WeakestForearmPronation 0.70* 0.79*StrongestArmExtension 0.83* 0.88*WeakestArmExtension 0.81* 0.87*
IsolatedTrunk 0.54* 0.61*Arm+Trunk 0.73* 0.78*
• 32International‐levelwheelchairtrackracers
• ClassesT54‐T51
• Sixisometricstrengthtests
Connick,M.J.,Beckman,E.,Vanlandewijck,Y.,Malone,L.,Blomqvist,S.andTweedy,S.(Underreview).Novelisometricstrength measuresproduceavalidandevidence‐basedclassificationstructureforwheelchairtrackracing:Aclusteranalysis.BritishJournalofSportsMedicine
• 4clusters• Differencesfoundinthe6strengthtestsand2performance‐relatedoutcomes
IDENTIFICATIONOFACLASSSTRUCTURE
AIM
Toevaluatethevalidityofstatisticalrecommendersystemsforallocatingclassinwheelchairtrackracingathletes.
• K-nearest neighbour• Discriminant analysis• Artificial Neural Network• Naïve Bayes• Classification Decision Tree
METHODS
Original Data
Sampling n = 1200 (Gaussian Copula)
Cluster analysis (k = 4)
Evaluate recommender
systems (10-fold cross validation)
Recommender system
Final Prediction
RESULTS
RESULTS
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k‐nearestneighbour
discriminantanalysis
artificialneuralnetwork
classificationtree
naïvebayes
Error(%
)
Classificationmethod
METHOD
Original Data
Recommender1
Sampling n = 1200
(Gaussian Copula)
Cluster analysis (k = 4)
Evaluate recommender
systems
Recommender2
•••
Majority vote Final predictions
Recommender Final Prediction
Recommender3
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k‐nearestneighbour
discriminantanalysis
artificialneuralnetwork
ensemble
Error(%
)
Classificationmethod
RESULTS
• Inthispopulation,statisticalrecommendersystemsprovidevalidtoolsthatclassifierscanusetoallocateclass
• However,thiswasproof‐of‐conceptandphilosophicaldiscussionsareneeded– Potentialtranslationofthesemethods
• Preciseparametersmustbecalculatedinalarge‐scalestudy(i.e.testcloseto100%ofthepopulation)
• Influenceoftrainingandintentionalmisrepresentation• Remember,retainexpertopinionandathletecontext
DISCUSSION
THANKYOU
Acknowledgments:
• InternationalParalympicCommittee• TheZayhed HigherOrganisation forHumanitarianCareandSpecialNeeds• Ergotest• Theathletes
• SeanTweedy,MarkConnickandEmmaBeckmanaremembersoftheIPCClassificationResearchandDevelopmentCentre(PhysicalImpairments),whichissupportedbytheInternationalParalympicCommittee.
