mark connick, emma beckman, sean tweedy the university of

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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1

2

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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.