CVLT-II short form forced choice recognition in a clinical dementia sample: Cautions for performance validity assessment
Bibliographic record
Abstract
Performance validity tests are susceptible to false positives from genuine cognitive impairment (e.g., dementia); this has not been explored with the short form of the California Verbal Learning Test II (CVLT-II-SF). In a memory clinic sample, we examined whether CVLT-II-SF Forced Choice Recognition (FCR) scores differed across diagnostic groups, and how the severity of impairment [Clinical Dementia Rating Sum of Boxes (CDR-SOB) or Mini-Mental State Examination (MMSE)] modulated test performance. Three diagnostic groups were identified: subjective cognitive impairment (SCI; n = 85), amnestic mild cognitive impairment (a-MCI; n = 17), and dementia due to Alzheimer’s Disease (AD; n = 50). Significant group differences in FCR were observed using one-way ANOVA; post-hoc analysis indicated the AD group performed significantly worse than the other groups. Using multiple regression, FCR performance was modeled as a function of the diagnostic group, severity (MMSE or CDR-SOB), and their interaction. Results yielded significant main effects for MMSE and diagnostic group, with a significant interaction. CDR-SOB analyses were non-significant. Increases in impairment disproportionately impacted FCR performance for persons with AD, adding caution to research-based cutoffs for performance validity in dementia. Caution is warranted when assessing performance validity in dementia populations. Future research should examine whether CVLT-II-SF-FCR is appropriately specific for best-practice testing batteries for dementia.
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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 | 0.277 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".