DEVELOPMENT OF THE WAIS-III GENERAL ABILITY INDEX ESTIMATE (GAI-E)
Bibliographic record
Abstract
The WAIS-III General Ability Index (GAI; Tulsky, Saklofske, Wilkins, & Weiss, 2001) is a recently developed, 6-subtest measure of global intellectual functioning. However, clinical use of the GAI is currently limited by the absence of a method to estimate premorbid functioning as measured by this index. The purpose of this study was to develop regression equations to estimate GAI scores from demographic variables and WAIS-III subtest performance. Participants consisted of those subjects in the WAIS-III standardization sample that has complete demographic data (N=2,401) and were randomly divided into two groups. The first group (n=1,200) was used to develop the formulas (i.e., Development group) and the second (n=1,201) group was used to validate the prediction algorithms (i.e., Validation group). Demographic variables included age, education, ethnicity, gender and region of country. Subtest variables included vocabulary, information, picture completion, and matrix reasoning raw scores. Ten regression algorithms were generated designed to estimate GAI. The GAI-Estimate (GAI-E) algorithms accounted for 58% to 82% of the variance. The standard error of estimate ranged from 6.44 to 9.57. The correlations between actual and estimated GAI ranged from r=.76 to r=.90. These algorithms provided accurate estimates of GAI in the WAIS-III standardization sample. Implications for estimating GAI in patients with known or suspected neurological dysfunction is discussed and future research is proposed.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".