Clinical utility of WAIS-IV ‘excessive decline from premorbid functioning’ scores to detect invalid test performance following traumatic brain injury
Bibliographic record
Abstract
Objective: Excessive Decline from Premorbid Functioning (EDPF), an atypical discrepancy between demographically predicted and obtained Wechsler Adult Intelligence Scale-4th Edition (WAIS-IV) scores, has been recently proposed as a potential embedded performance validity test (PVT). This study examined the clinical utility of EDPF scores to detect invalid test performance following traumatic brain injury (TBI).Methods: Participants were 194 U.S. military service members who completed neuropsychological testing on average 2.4 years (SD = 4.0) following uncomplicated mild, complicated mild, moderate, severe, or penetrating TBI (Age: M = 34.0, SD = 9.9). Using TBI severity and PVT performance (i.e., PVT Pass/Fail), participants were classified into three groups: Uncomplicated Mild TBI-PVT Fail (MTBI-Fail; n = 21), Uncomplicated Mild TBI-PVT Pass (MTBI-Pass; n = 94), and Complicated Mild to Severe/Penetrating TBI-PVT Pass (CM/STBI-Pass; n = 79). Seven EDPF measures were calculated by subtracting WAIS-IV obtained index scores from the demographically predicted scores from the Test of Premorbid Functioning (TOPF). Cutoff scores to detect invalid test performance were examined for each EDPF measure separately.Results: The MTBI-Fail group had higher scores than the MTBI-Pass and CM/STBI-Pass groups on five of the seven EDPF measures (p<.05). Overall, the EDPF measure using the Processing Speed Index (EDPF-PSI) was the most useful score to detect invalid test performance. However, sensitivity was only low to moderate depending on the cutoff score used.Conclusions: These findings provide support for the use of EDPF as an embedded PVT to be considered along with other performance validity data when administering the WAIS-IV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".