RBANS memory percentage retention: No evidence of incremental validity beyond RBANS scores for diagnostic classification of mild cognitive impairment and dementia and for prediction of daily function
Bibliographic record
Abstract
RBANS percentage retention scores may be useful for diagnosis, but their incremental validity is unclear. Percentage retention versus RBANS immediate and delayed memory subtests and delayed index scores were compared for diagnostic classification and for prediction of function. Data from 173 memory clinic patients with an interdisciplinary diagnosis (no cognitive impairment, amnestic mild cognitive impairment [aMCI], and dementia due to Alzheimer's disease [AD]) and complete RBANS data were analyzed. Across diagnostic contrasts, list percentage retention classification accuracy was similar to List Learning delayed recall, but below the Delayed Memory Index (DMI). Similarly, for classifying no cognitive impairment versus aMCI or dementia due to AD, story percentage retention was similar to Story Memory subtests and below the DMI. For classifying aMCI versus AD; however, Story Memory exceeded the DMI, but was similar to Story Memory subtest scores. Similarly, for prediction of function percentage retention measures did not predict variance beyond that predicted by the RBANS subtest or index scores. In sum, there is no evidence that calculation of percentage retention for RBANS adds clinical utility beyond those provided by the standard RBANS scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".