The utility of Matrix Reasoning as an embedded performance validity indicatory in youth athletes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".