The D-KEFS Trails as performance validity tests.
Bibliographic record
Abstract
This study was designed to examine the potential of the Delis-Kaplan Executive System (D-KEFS) version of the Trail Making Test (TMT) as a performance validity test (PVT). Data were collected from a mixed clinical sample of 157 consecutively referred outpatients (49% male, MAge = 47.1, MEducation = 13.6) undergoing neuropsychological assessment at an academic medical center in the northeastern United States. Sensitivity and specificity of the D-KEFS Trails to psychometrically defined invalid responding was calculated across various cutoffs and criterion PVTs. The D-KEFS Trails produced classification accuracy comparable to the original version of the TMT, hovering around the "Larrabee limit" (.50 sensitivity at .90 specificity). Different cutoffs (age-corrected scaled score ≤5 on Trails 1-3, ≤4 on Trails 4 and ≤8 on Trails 5) were needed to achieve the same classification accuracy across the five trials. Combining multiple cutoffs improved the signal detection performance. The study provides preliminary evidence of the utility of D-KEFS Trails as a PVT. Embedded PVTs are valuable, because they make a multivariate approach to validity assessment feasible. Combining validity indicators is superior to relying on single cutoffs. (PsycINFO Database Record
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".