Clinical utility of embedded performance validity tests on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) following mild traumatic brain injury
Bibliographic record
Abstract
This study evaluated the clinical utility of two embedded performance validity tests (PVTs) developed for the Repeatable Battery for the Assessment of Neuropsychological Status: the Effort Index (EI) and the Effort Scale (ES) in mild traumatic brain injury (TBI) patients. Participants were 250 military service members (94.0% male; Age: M = 28.4, SD = 7.6) evaluated following mild TBI on average 7.4 months (SD = 15.6) post-injury. Participants were divided into two groups based on their performance on the Test of Memory Malingering: PVT-Pass, n = 193; PVT-Fail, n = 57. For the EI, recommended cut-offs for extremely probable, highly probable, and probable poor effort were established. A cut-off score of >3 resulted in low sensitivity (.14), high specificity (.99) and positive predictive power (.94), and moderate negative predictive power (.68) and is recommended for identifying highly probable poor effort. For both the EI and ES, cut-offs for probable poor effort were identified; however, classification accuracy was not much improved relative to simply using the sum of the List Recognition and Digit Span raw scores to classify poor effort. It is acknowledged that the use of a different criterion would likely have resulted in different findings. Nevertheless, findings support the use of the EI and the ES as a "red flag" for possible poor effort in mild TBI patients, but suggest that, in most cases, additional PVTs would be required to accurately rule poor effort in or out.
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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 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".