Revised Neurobehavioral Scales of the MMPI: Sensitivity and Specificity in Traumatic Brain Injury
Bibliographic record
Abstract
The ability of 23 previously identified Minnesota Multiphasic Personality Inventory (MMPI) "neurologic content" items to distinguish between individuals with traumatic brain injury (TBI; n = 32) or spinal cord injury (SCI; n = 17) was examined. Principal-components analysis of the 23 items revealed three conceptually coherent, nonoverlapping, and uncorrelated factors (Cognitive, Somatic, Inactivity) that together accounted for 44% of the total variance. Coefficients of internal consistency for the factors were in the moderate to high range. Together, the factors were named the Revised Neurobehavioral Scales of the MMPI. The group with TBI scored significantly higher on the Cognitive scale and significantly lower on the Inactivity scale than the group with SCI (with or without depression as a covariate). The Glasgow Coma Scale correlated significantly and negatively with the Cognitive scale in the group with TBI. Discriminant function analysis revealed that together the scales correctly classified individuals with sensitivity and a positive predictive value (with respect to TBI) of 87% and 81%, respectively. Specificity and a negative predictive value (with respect to SCI) were 68% and 76%, respectively. The overall rate of correct classification of individual cases was 80% (with or without depression in the analysis). The Cognitive scale alone correctly classified individuals in the group with TBI with a positive predictive value of 84%. Findings are discussed in terms of the discriminative validity and potential utility of TBI-related MMPI items, as well as the issue of "neurocorrection" of the MMPI (or MMPI-2) in verified cases of TBI.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".