Clinical utility of the Conners’ Continuous Performance Test-II to detect poor effort in U.S. Military personnel following traumatic brain injury.
Bibliographic record
Abstract
The purpose of this study is to examine the clinical utility of the Conners' Continuous Performance Test (CPT-II) as an embedded marker of poor effort in military personnel undergoing neuropsychological evaluations following traumatic brain injury. Participants were 158 U.S. military service members divided into 3 groups on the basis of brain injury severity and performance (pass/fail) on 2 symptom validity tests: Mild Traumatic Brain Injury (MTBI)-Pass (n = 87), MTBI-Fail (n = 42), and severe traumatic brain injury (STBI)-Pass (n = 29). The MTBI-Fail group performed worse on the majority of CPT-II measures compared with both the MTBI-Pass and STBI-Pass groups. When comparing the MTBI-Fail group and MTBI-Pass groups, the most accurate measure for identifying poor effort was the Commission T score. When selected measures were combined (i.e., Omissions, Commissions, and Perseverations), there was a very small increase in sensitivity (from .26 to .29). When comparing the MTBI-Fail group and STBI-Pass groups, the most accurate measure for identifying poor effort was the Omission and Commissions T score. When selected measures were combined, sensitivity again increased (from .24 to .45). Overall, these results suggest that individual CPT-II measures can be useful for identifying people who are suspected of providing poor effort from those who have provided adequate effort. However, due to low sensitivity and modest negative predictive power values, this measure cannot be used in isolation to detect poor effort, and is largely useful as a test to "rule in," not "rule out" poor effort.
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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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".