Verbal-performance intelligence quotient discrepancies on the Wechsler scales: Are still useful? Literature review and clinical implications
Bibliographic record
Abstract
Verbal-performance intelligence quotient (IQ) discrepancies on the Wechsler scales have been the subject of several studies. In most cases these studies have focused only on the cognitive aspects (visuospatial abilities, academic achievement, language), prevalently on behavioural and attentional aspects, or principally on psychopathological and psychosocial spheres. The relevant literature demonstrate the considerable complexity of the various neuropsychological, behavioural and psychopathological traits presented by subjects showing significant discrepancies between verbal and performance IQ on the Wechsler scales. Starting with a brief review of the literature on this topic, this study aims to offer various points for discussion about the clinical implications of Wechsler scale discrepancies and about the perspectives for research in syndromes and disorders closely associated with them (developmental right-hemisphere syndrome, non-verbal learning disabilities, attention-deficit/hyperactivity disorder). Taking into consideration new knowledge on this topic, our aim is to look in depth at the practical and theoretical implications of this scale, which is very widely used in clinical practice. A limitation of this study is that in recent editions of Wechsler tests (e.g. WISC-m, WAIS-III) new factors replaced verbal intelligence quotient (VIQ) and performance intelligence quotient (PIQ) scores. Unfortunately in Italy, as well as in other countries, WISC-R and WAIS-R are the only instruments available to assess cognitive skills. The new factors, however, could be extrapolated from specific subtests of at present available Wechsler scales in our clinical practice, as suggested by different authors.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".