Bibliographic record
Abstract
One-hundred and thirty-five children between the ages of 7 and 18 years were evaluated clinically. Their diagnoses included Fetal Alcohol Syndrome (FAS) or Effects, Schizophrenia, Bipolar Mood Disorder, various neurological diseases, Attention Deficit Hyperactivity Disorder (ADHD), Conduct Disorder, Oppositional-Defiant Disorder and learning disabilities. As part of a comprehensive neuropsychological assessment, the children were given the Word Memory Test (WMT; Green, Allen, & Astner, 1996; Green & Astner, 1995), containing various subtests which measure, respectively, effort and verbal memory. Although age and verbal intelligence are known to affect scores on most ability tests, they were not found to be significant determinants of WMT effort scores. Younger children did not score any lower on the effort subtests than older children. The children scored as well as a group of parents seeking custody of their children and they scored higher than adult patients with mild head injuries. The computerized WMT requires some basic reading skills and some children with lower than a grade 3 reading level scored at a relatively low level on the effort subtests. The current data suggest that most children with at least a grade 3 reading level can pass the WMT using the adult criteria. It is concluded that the WMT is potentially useful in the evaluation of effort during pediatric neuropsychological evaluations. Further research is needed to replicate these findings and to develop child norms for the memory subtests.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".