Embedded Performance Validity on the CVLT-C for Youth with Neurological Disorders
Bibliographic record
Abstract
Embedded validity measures can screen for possible noncredible performance, but there is a paucity of literature with youth who have neurological disorders. The purpose of this study is to examine the California Verbal Learning Test, Children's Version (CVLT-C) recognition discriminability (RD) score as an embedded validity marker in a sample of youth with neurological diagnoses. Youth between 5-16 years old (N = 294; mean age = 11.3, SD = 3.4) completed the CVLT-C and the Test of Memory Malingering (TOMM). Overall, 5.4% (n = 16) scored below the established cutoff on the TOMM; they were younger, had lower intellectual abilities, and worse performance on nearly all CVLT-C scores than those who scored above the TOMM cutoff. Using the CVLT-C RD score of z ≤ -0.5 (Baker et al. 2004), our sample had a sensitivity = .81 and specificity = .67. Using z ≤ -3.0 provided sensitivity at .44 with specificity at .90. A lower cutoff score of z ≤ -3.0 for CVLT-C RD is necessary in youth with neurological diagnoses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".