DIETARY TREATMENT OF EPILEPSY. PRACTICAL IMPLEMENTATION OF KETOGENIC THERAPY. 2012. Edited by Elizabeth Neal. Published by Wiley-Blackwell. 241 pages. C$80 approx.
Bibliographic record
Abstract
The ketogenic diet has been used for nearly 100 years in the treatment of epilepsy.However, it has experienced a considerable resurgence in use since the early 1990's.It is now offered as an important therapeutic option in most pediatric epilepsy centers worldwide, and its use in adults with medically intractable epilepsy is rapidly growing.More recent modifications including the modified Atkins diet and Low Glycemic Index diet offer improved tolerability, and appear to have similar efficacy to the classical ketogenic diet."Dietary Treatment of Epilepsy: Practical Implementation of Ketogenic Therapy" fills an essential niche both for health care providers, parents and persons with epilepsy, providing a very practical and concise overview of several aspects of dietary therapy for epilepsy.It is edited by Elizabeth Neal, a dietician and author on the first randomized controlled study of the ketogenic diet for treatment of epilepsy, who is well-recognized internationally for her expertise in ketogenic diet therapy.The chapters are written by a panel of international physician and dietician experts.This book is subdivided into three sections:
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.035 |
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".