Predictive Accuracy of the Wide Range Assessment of Memory and Learning in Children With Attention Deficit Hyperactivity Disorder and Reading Difficulties
Bibliographic record
Abstract
The predictive accuracy of the Wide Range Assessment of Memory and Learning (WRAML; Sheslow & Adams, 1990) over and above more standardized diagnostic tools in children with attention deficit hyperactivity disorder (ADHD) and reading disabilities (RD) was examined. Fifty-three children with ADHD, 63 with RD, 63 with ADHD-RD, and 112 normal comparison children were administered the WRAML, the Wechsler Intelligence Scale for Children-Third Edition (WISC-III; Wechsler, 1991), the Achenbach (1991) Child Behavior Checklist (CBCL), and the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R; Woodcock & Johnson, 1989). Results of a series of discriminant function analyses revealed that the academic, intellectual, and behavioral measures could correctly classify 73.1% of children, but the WRAML subtests alone were able to correctly classify only 58.5% of participants. Combining all of the memory, academic, intellectual, and behavioral measures resulted in 77.5% of cases being correctly classified. These results suggest that the use of a measure of memory functioning such as the WRAML did not significantly improve the predictive accuracy of a diagnosis of ADHD, RD, or both over and above more standard diagnostic academic, intellectual, and behavioral measures.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".