Bibliographic record
Abstract
In the last line of the forward to Pediatric Neuropsychology, Arthur Benton stated that “pediatric neuropsychology has come of age.” This volume provides ample support for this statement. It demonstrates how the field of pediatric neuropsychology has developed and matured, but it also reminds us of the many questions that remain unanswered. Its focus is on the neurobehavioral sequelae of various medical disorders such as epilepsy, meningitis, and diabetes. It does not address, however, the neurobehavioral outcomes associated with attention-deficit/hyperactivity disorder, learning disabilities and autism spectrum disorders. As indicated by Byron Rourke in his commentary (chapter 20), “these are important areas of pediatric neuropsychological research that merit attention because of their theoretical and clinical relevance” (p. 476). I agree with Dr. Rourke's statement that they should be covered in a book that deals with pediatric neuropsychology and would also encourage the editors to include them in a second edition should it be forthcoming. Despite this shortcoming, this book is an excellent reference for both researchers and clinicians in pediatric neuropsychology. Further, because of its multidisciplinary nature it may be of interest to other professionals who work with and treat children with various medical disorders that have neurobehavioral sequelae such as pediatricians, pediatric neurologists, pediatric psychiatrists and medical geneticists.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".