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The utility of Matrix Reasoning as an embedded performance validity indicatory in youth athletes

2019· article· en· W2977776994 on OpenAlexfundno aff
Jennifer S. Adler, Ryan C. Thompson, Naomi Kaswan, Rayna B. Hirst

Bibliographic record

VenueNeurology · 2019
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsCutoffAthletesPsychologyReceiver operating characteristicRaven's Progressive MatricesArea under the curvePhysical therapyStatisticsAudiologyInternal medicineCognitionMedicinePsychiatryMathematics

Abstract

fetched live from OpenAlex

Objective The present study assessed Matrix Reasoning (MR) as an embedded validity indicator (EVI) in youth athletes vulnerable to sport-related concussion, using performance on the Test of Memory Malingering (TOMM) to operationalize effort. Background Matrices tasks have been examined as EVI for pediatric neropsychological assessment (NA; McKinsey, Prieler, & Raven, 2003), and recent literature suggests a cutoff T-score of 43 for MR in the Wechsler Abbreviated Scale of Intelligence, Second Edition (WASI-II) may demonstrate utility within youth athletes completing baseline NA. Design/Methods 103 youth athletes (76% male, Mage = 12.14) completed a NA, including MR (cutoff T = 43) and TOMM (cut-offs = 45 and 49). Sensitivity and specificity for MR were calclated. Reciever operator characteristics (ROC) curve analysis determined whether MR performance accurately categorized participants9 effort (represented by TOMM performance). Results MR (cut-off T = 43; Sussman et al., 2019) produced sensitivity of 9.09% and specificity of 91.36% in predicting TOMM Trial 1 performance (TOMM1; AUC = 0.449) and 0.00% and 91.18% in predicting TOMM Trial 2 (TOMM2; AUC = 0.074). As a TOMM2 cut-off of 49 offers greater sensitivity to inadequate effort, a further analysis showed MR yielded sensitivity of 0.00% and specificity of 91.00% (AUC = 0.330) in predicting TOMM2 performance with the more conservative cutoff. Conclusions MR is an adequate EVI in predicting sufficient effort on TOMM, detecting true effortful performance; however, it was inadequate in detecting true non-effortful performance. A more stringent TOMMM cutoff did not improve sensitivity; thus, MR exhibited poor detection of inadequate effort. Overall, MR has utility as an EVI to support adequate effort in youth athlete populations but should not be used independently. This finding is clinically important because adequate effort at baseline is imperative in determining recovery from concussion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.294
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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