P.057 A review of a twenty-seven year experience with the Ketogenic diet: lessons learned and moving forward
Bibliographic record
Abstract
Background: Although the history of the ketogenic diet dates back centuries, with the advancement of anti-seizure medications, the use of the diet for epilepsy declined. It was not until the early 1990s that there was a resurgence of the diet as an adjunct therapy to anti-seizure medication. In 1998, the Montreal Children’s Hospital introduced the ketogenic diet to a child with drug resistant epilepsy. Shortly after, a presentation of the ketogenic diet at hospital Grand Rounds met much skepticism. However, over time the diet has developed into a well-established treatment option for children with drug resistant epilepsy. Two hundred children have since utilized the diet at the Montreal Children’s Hospital. Methods: A review of patient files since the initiation of the program was undertaken. Data was extracted regarding adverse effects, common errors in both hospital and home setting, risks for unfavorable outcomes and parental concerns Results: The development of a rigorous protocol has reduced potential adverse effects, inadvertent complications from errors have improved and parent satisfaction enhanced. Conclusions: This poster will demonstrate how an interdisciplinary approach for a ketogenic diet protocol, involving an advanced Practice Nurse, nutritionist, neurologist and parent, resulted in improved care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".