Sensitivity of the Test of Memory Malingering and the Nonverbal Medical Symptom Validity Test: A Replication Study
Bibliographic record
Abstract
The current investigation sought to replicate and extend the findings of Green (in press Green , P. , Flaro , F. , Brockhaus , R. , & Montijo , J. ( in press ). Performance on the WMT, MSVT, & NV-MSVT in children with developmental disabilities and in adults with mild traumatic brain injury . In C. R. Reynolds & A. Horton (Eds.), Detection of malingering during head injury litigation () , 2nd ed. . New York , NY : Plenum Press . [Google Scholar]), which demonstrated superior sensitivity of the Nonverbal Medical Symptom Validity Test (NV-MSVT) relative to the Test of Memory Malingering (TOMM) in the detection of suboptimal effort during neuropsychological assessment. Nearly twice as many examinees failed the NV-MSVT than the TOMM. Profile analyses of the NV-MSVT demonstrated patterns suggestive of inconsistent effort in those who failed the NV-MSVT but passed the TOMM. A classification analysis employing the Word Memory Test and Medical Symptom Validity Test as external criteria for poor effort showed that the NV-MSVT is substantially more sensitive to poor effort than the TOMM and maintains an acceptable false-positive rate. Overall, results closely matched those of the Green (in press Green , P. , Flaro , F. , Brockhaus , R. , & Montijo , J. ( in press ). Performance on the WMT, MSVT, & NV-MSVT in children with developmental disabilities and in adults with mild traumatic brain injury . In C. R. Reynolds & A. Horton (Eds.), Detection of malingering during head injury litigation () , 2nd ed. . New York , NY : Plenum Press . [Google Scholar]) study and extend the evidence that the NV-MSVT possesses better sensitivity to poor effort than the TOMM.
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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 | 0.029 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".