BRAIN TUMOR IMMUNOTHERAPY. 2000. Edited by Linda M. Liau, Donald P. Becker, Timothy F. Cloughesy, Darell D. Bigner. Published by Humana Press. 373pages. C$198.45 approx.
Bibliographic record
Abstract
several laboratory animal models which have been used to study the development of long-term memory in marine mollusks, the development of the rat motor cortex and the visual system.Part III is the largest section and has seven chapters that describe neurobehavioural changes in a variety of early brain disorders which include congenital malformations, surgically induced lesions for treatment of epilepsy and tumors, focal infarctions and autism.Neurobehavioural data as well as observations from quantitative functional magnetic resonance imaging are discussed.The two chapters in the final section (Part IV), address therapeutic interventions which are based on the theory of plasticity within the central nervous system.Proposals for effective interventions for high risk infants of very-low-birthweight are described and exciting results are reported, which suggest that it may be possible to achieve improvements in performance with appropriate interventional strategies.The final chapter summarizes and integrates our current understanding of biological brain development, learning and neuroplasticity and raises provocative questions for future research.Although many of the contributors to this text are basic neuroscientists, the text manages to maintain a remarkable balance between experimental observations and clinical applications.In my opinion, this text succeeds in fulfilling its primary objective of bridging the gap between neuroscientists and clinicians and fostering collaborative research between the disciplines.This text provides fascinating reading for clinician-researchers, as well as for pediatric neurologists, pediatricians and therapists who are concerned with the causes and management of disabling developmental disorders in childhood.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.087 |
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".