Examining the Relationship Between WAIS-III Premorbid Intellectual Functioning and WMS-III Memory Ability to Evaluate Memory Impairment
Bibliographic record
Abstract
The purpose of this study was to extend previous research by Lange and Chelune (2006 Lange , R. T. , & Chelune , G. J. ( 2006 ). Application of New WAIS-III/WMS-III discrepancy scores for evaluating memory functioning: Relationship between intellectual and memory abilities . Journal of Clinical and Experimental Neuropsychology , 28 , 592 – 604 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) by evaluating the clinical utility of GAI-memory discrepancy scores to detect memory impairment using estimated premorbid GAI scores (i.e., GAI-E) rather than obtained GAI scores. Participants were 34 patients with Alzheimer's-type dementia and a sub-sample of 34 demographically matched participants from the WAIS-III/WMS-III standardization sample. GAI-memory discrepancy scores were more effective at differentiating Alzheimer's patients versus healthy controls when using estimated premorbid GAI scores than obtained GAI scores. However, GAI(E)-memory discrepancy scores failed to provide unique interpretive information beyond that which is gained from interpretation of the memory index scores alone. This was most likely due to the prevalence of obvious memory impairment in this patient population. Future research directions are discussed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".