Bibliographic record
Abstract
Models of Seizures and Epilepsy . A. Pitkänen, P. A. Schwartzkroin, and S. L. Moshé . Elsevier Academic Press , San Diego , 2006 , 687 pp . With the constant progress of the neurosciences and the mechanisms involved in epileptic seizures, this book on the animal models of seizures and epilepsy arrives at a very good time. The book is well written. The references are quite up to date (a number of references from 2004). This book reviews both the in vitro and in vivo models. It touches the technical and scientific aspects of the different models going from genetic to lesional models. More important, it looks at how to improve on the current animal models to answer some specific questions. The book has a number of strengths. All of the chapters stand alone quite well and could serve as an individual review for the model of your interest. More important, the chapters are grouped together in a manner that favors direct comparison between each model. This will be a significant advantage for scientists beginning in the field of animal models. It is hard to pick out some of the better chapters, because it all depends on your field of interest. The chapters on hyperthermic seizures by Dubé and Baram, freeze lesion by Luhmann, and kainate by Dudek et al. are all outstanding, but I might be a little biased. Outside of my direct field of interest, my other choices would include the one on pharmacologic models of generalized absence epilepsies by Cortez and Snead, as well as spontaneous epileptic mutations in the mouse by Noebels. All these chapters give the reader a thorough understanding of these different models. The book also contains a section about the technical aspects that can help validate models, such as seizure monitoring, brain imaging, behavioral and cognitive testing, as well as morphologic approaches. All these chapters are once again detailed reviews, which are more comprehensive than found in scientific articles and even review papers on methodology. Finally, the introduction and conclusion discuss the translational aspects of animal models on different aspects such as epileptogenesis, interictal state, ictogenesis, seizure termination, postictal state, long-term consequences, comorbidity, and last but not least, treatment efficacy. I agree that Drs. Schwartzkroin and Engel have hit the nail on the head when they discuss the need for having realistic goals and validating each model. I would recommend this book primarily to scientists in the field of epilepsy, but it also contains information of interest to many clinicians, residents, and fellows involved in a comprehensive epilepsy program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.351 | 0.281 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".