Cognitive Status & Dementia Severity in CVLT-II-SF Forced Choice Recognition: Implications for Effort Assessment
Bibliographic record
Abstract
Abstract Effort testing is critical to neuropsychological practice, including dementia assessment. Questions exist around whether cognitive status or impairment severity impacts effort test performance in this population. Presently, we examined whether scores on an embedded effort test - the California Verbal Learning Test II Short Form (CVLT-II-SF) Forced Choice Recognition (FCR) - differed across diagnostic cognitive status groups and how severity of impairment modulated test performance. In a sample of memory clinic patients, three cognitive status groups were identified: subjective cognitive impairment (SCI; n = 92), amnestic mild cognitive impairment (a-MCI; n = 18), and dementia due to Alzheimer’s Disease (AD; n = 70). Significant group differences in FCR performance were observed using one-way ANOVA (p < .001), with post-hoc analysis indicating the AD group performed significantly worse scores than the other groups. Using multiple regression, FCR performance was modelled as a function of cognitive status, impairment severity indexed MMSE, and their interaction, with a parallel analysis for the Clinical Dementia Rating Sum of Boxes (CDR-SOB) scores as an alternate severity measure. Results yielded significant main effects for MMSE (p = 0.019) and cognitive status (p = 0.026), as well as a significant interaction (p = 0.021). Thus, increases in impairment severity disproportionately impaired FCR performance for persons with AD, calling into question research-based cut scores for effort determination in dementia contexts. Corresponding CDR-SOB analyses were non-significant. Future research should examine whether CVLT-II-SF-FCR is an appropriately specific inclusion in a best-practice testing battery for evaluating effort in dementia populations.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".