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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".