Using the Memory Validity Profile (MVP) to detect invalid performance in youth with mild traumatic brain injury
Bibliographic record
Abstract
Performance validity tests (PVT) should be used when assessing youth with mild traumatic brain injury (MTBI). The goal of this study was to derive a new cutscore for determining invalid performance on the Memory Validity Profile (MVP) in youth with MTBI. Children and adolescents (N = 92; mean age =14.8 years, SD = 2.3, range =8–18) on average six months (SD = 3.6) post-MTBI were administered the MVP as part of their assessment. Two validated PVTs [Test of Memory Malingering (TOMM) and Medical Symptom Validity Test (MSVT)] were administered and used to group the sample into valid (n = 73, neither TOMM/MSVT failed) and invalid (n = 19, both TOMM/MSVT failed). New cutscores for the MVP to determine invalid performance in this sample were established using failure on both TOMM/MSVT as the criterion. MVP performance correlated significantly with failure on TOMM/MSVT. Youth with invalid performance had significantly lower MVP total scores and area under the curve was .80, suggesting good separation of groups. A cutscore of 31 or less on the MVP provided sensitivity of 63% for detecting invalid performance with 93% specificity. This study yields a promising new cutscore for the MVP that has good sensitivity and strong specificity for detecting invalid performance in youth with MTBI.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".