Neuropsychological assessment of children with cancer
Bibliographic record
Abstract
Introduction Neuropsychology, broadly defined, is the study of brain–behavior relationships. The term was coined by William Osler in the early 1900s and gained wider appeal in the 1960s. The field was influenced by pioneers in neuroanatomy, neurology, and physiology, who began to explore the brain's functionality (Broca, 1865; Hughlings-Jackson, 1931; Lashley, 1950; Wernicke, 1874). Modern neuropsychology represents a blend of careful clinical observation grounded in the pioneering work of Alexandr Luria (1973), and a more actuarial approach that utilizes psychometric instruments to describe and quantify an individual's functioning (Halstead, 1947; Reitan, 1974). Neuropsychology has become a science of human behavior as it is influenced by brain functioning and by social, psychological, and cultural contexts. Pediatric neuropsychologists are concerned with developmental issues and take into account the genetic, medical, environmental, behavioral, and sociocultural influences that impact the maturation of a child (Baron, 2004). The human nervous system is never static and development occurs across the lifespan. However, the rapidity of development in childhood and adolescence calls for a specific developmental focus when conducting evaluations with this age group. At birth, infants have more than 100 billion neurons (Berger, 2005). In the first 2 years of life the brain undergoes a period termed transient exuberance when as many as 15 000 new connections are established per neuron (Thompson, 2000). Following this period of rapid growth, there is a period of rapid elimination of synapses called “pruning” that peaks in adolescence and is variable across different brain regions (Kolb & Wishaw, 2003).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".