RBANS Embedded Measures of Suboptimal Effort in Dementia: Effort Scale Has a Lower Failure Rate than the Effort Index
Bibliographic record
Abstract
The importance of evaluating effort in neuropsychological assessments has been widely acknowledged, but measuring effort in the context of dementia remains challenging due to the impact of dementia severity on effort measure scores. Two embedded measures have been developed for the repeatable battery for the assessment of neuropsychological status (RBANS; Randolph, C., Tierney, M. C., Mohr, E., & Chase, T. N. (1998). The repeatable battery for the assessment of neuropsychological status (RBANS): Preliminary clinical validity. Journal of Clinical and Experimental Neuropsychology, 20 (3), 310-319): the Effort Index (EI; Silverberg, N. D., Wertheimer, J. C., & Fichtenberg, N. L. (2007). An effort index for the repeatable battery for the assessment of neuropsychological status (RBANS). Clinical Neuropsychologist, 21 (5), 841-854) and the Effort Scale (ES; Novitski, J., Steele, S., Karantzoulis, S., & Randolph, C. (2012). The repeatable battery for the assessment of neuropsychological status effort scale. Archives of Clinical Neuropsychology, 27 (2), 190-195). We explored failure rates on these effort measures in a non-litigating mixed dementia sample (N = 145). Failure rate on the EI was high (48%) and associated with dementia severity. In contrast, failure on the ES was 14% but differed based on type of dementia. ES failure was low (4%) when dementia was due to Alzheimer disease (AD), but high (31%) for non-AD dementias. These data raise concerns about use of the RBANS embedded effort measures in dementia evaluations.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".