Bibliographic record
Abstract
Clinicians should note that there is considerable variability in the reliabilities of the index and subtest scores derived from the third editions of the Wechsler Adult Intelligence Scale (WAIS-III) and the Wechsler Memory Scale (WMS-III). The purpose of this article is to review these reliabilites and to illustrate how they can be used to interpret change in patients' performances from test to retest. The WAIS-III IQ and Index scores are consistently the most reliable scores, in terms of both internal consistency and test–retest reliability. The most internally consistent WAIS-III subtests are Vocabulary, Information, Digit Span, Matrix Reasoning, and Arithmetic. Information and Vocabulary have the highest test–retest reliability. On the WMS-III, the Auditory Immediate Index, Immediate Memory Index, Auditory Delayed Index, and General Memory Index are the most reliable, in terms of both internal consistency and test–retest reliability. The Logical Memory I and Verbal Paired Associates I subtests are the most reliable. Data from three clinical groups (i.e., Alzheimer's disease, chronic alcohol abuse, and schizophrenia) were extracted from the Technical Manual [Psychological Corporation (1997). WAIS-III/WMS-III Technical Manual. San Antonio: Harcourt Brace] for the purpose of calculating reliable change estimates. A table of confidence intervals for test–retest measurement error is provided to help the clinician determine if patients have reliably improved or deteriorated on follow-up testing.
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 | 0.030 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".