Aggregating Validity Indicators Embedded in Conners' CPT-II Outperforms Individual Cutoffs at Separating Valid from Invalid Performance in Adults with Traumatic Brain Injury
Bibliographic record
Abstract
Continuous performance tests (CPT) provide a useful paradigm to assess vigilance and sustained attention. However, few established methods exist to assess the validity of a given response set. The present study examined embedded validity indicators (EVIs) previously found effective at dissociating valid from invalid performance in relation to well-established performance validity tests in 104 adults with TBI referred for neuropsychological testing. Findings suggest that aggregating EVIs increases their signal detection performance. While individual EVIs performed well at their optimal cutoffs, two specific combinations of these five indicators generally produced the best classification accuracy. A CVI-5A ≥3 had a specificity of .92-.95 and a sensitivity of .45-.54. At ≥4 the CVI-5B had a specificity of .94-.97 and sensitivity of .40-.50. The CVI-5s provide a single numerical summary of the cumulative evidence of invalid performance within the CPT-II. Results support the use of a flexible, multivariate approach to performance validity assessment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".