Clinical Validation of the General Ability Index—Estimate (GAI-E): Estimating Premorbid GAI
Bibliographic record
Abstract
The clinical utility of the General Ability Index—Estimate (GAI-E; Lange, Schoenberg, Chelune, Scott, & Adams, 2005 Lange , R. , Schoenberg , M. , Chelune , G. , Scott , J. , & Adams , R. ( 2005 ). Development of the WAIS-III General Ability Index Estimate (GAI-E) The Clinical Neuropsychologist , 19 , 73 – 86 . [INFOTRIEVE] [CSA] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) for estimating premorbid GAI scores was investigated using the WAIS-III standardization clinical trials sample (The Psychological Corporation, 1997 The Psychological Corporation . ( 1997 ). Wechsler Adult Intelligence Scale—3rd Edition/Wechsler Memory Scale—3rd Edition (WAIS-III/WMS-III) Technical Manual . San Antonio , TX : Author . [Google Scholar]). The GAI-E algorithms combine Vocabulary, Information, Matrix Reasoning, and Picture Completion subtest raw scores with demographic variables to predict GAI. Ten GAI-E algorithms were developed combining demographic variables with single subtest scaled scores and with two subtests. Estimated GAI are presented for participants diagnosed with dementia (n = 50), traumatic brain injury (n = 20), Huntington's disease (n = 15), Korsakoff's disease (n = 12), chronic alcohol abuse (n = 32), temporal lobectomy (n = 17), and schizophrenia (n = 44). In addition, a small sample of participants without dementia and diagnosed with depression (n = 32) was used as a clinical comparison group. The GAI-E algorithms provided estimates of GAI that closely approximated scores expected for a healthy adult population. The greatest differences between estimated GAI and obtained GAI were observed for the single subtest GAI-E algorithms using the Vocabulary, Information, and Matrix Reasoning subtests. Based on these data, recommendations for the use of the GAI-E algorithms are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".