California Verbal Learning Test Indicators of Suboptimal Performance in a Sample of Head-Injury Litigants
Bibliographic record
Abstract
Cutoff scores suggested by Millis, Putnam, Adams, and Ricker (1995) for detecting suboptimal performance on indices from the California Verbal Learning Test (CVLT) were evaluated using data from 193 compensation-seeking participants. All participants claimed to have suffered a blow to the head in an accident causing subsequent deterioration in cognitive function. The participants were divided into those with negligible or possible mild brain injuries and those with clear evidence of moderate to severe brain injuries. In addition to the CVLT, all participants were administered the Computerized Assessment of Response Bias (CARB), a two-alternative forced choice test of recognition memory that is used to detect feigned cognitive impairment. For all CVLT indices, the distributions of outcome (valid vs. suboptimal performance) was unrelated to age and brain injury severity, and only weakly associated with education. However, a significantly higher proportion of males than females obtained scores in the suboptimal performance range. The CVLT indices were not fully redundant with each other with respect to binary participant classifications; substantial disagreement between pairwise classifications was found among those participants who obtained at least one score in the suboptimal performance range. CVLT index classifications were also found to be non-redundant with classifications based on CARB scores. The CVLT may thus add useful data over and above that obtained from symptom validity testing. However, the data suggest that the use of the strategy where any one or more below-cutoff CVLT scores are considered a positive indicator of suboptimal performance may be associated with a higher than acceptable false-positive error rate.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".