PREDICTORS OF A SUCCESSFUL OUTCOME IN CHILDREN ON THE KETOGENIC DIET
Bibliographic record
Abstract
Objectives: The ketogenic diet (KD) has been shown repeatedly to be efficacious in treating intractable seizures in children. However, it causes many adverse effects and alters an entire family's lifestyle. Therefore, this study sought to identify potential predictors of a successful outcome in children treated with the KD. Methods: A retrospective chart review of eleven children who had been treated with the KD. A baseline EEG and blood sample, consisting of CBC, electrolytes, creatinine, urea, glucose, triglycerides, cholesterol, HDL, LDL, and cholesterol/HDL ratio, were obtained prior to the start of the diet. Height, weight and the number of antiepileptic medications were also noted pre-KD. A successful outcome was defined as >50% reduction in seizure frequency. Results: The effectiveness of the diet was similar to that seen in a previous meta-analysis: 55% of children had >50% reduction in seizure frequency. Children with higher cholesterol pre-KD and those who stayed on the diet longer were more likely to have a successful outcome. No significant difference in outcome was found related to age, gender, seizure frequency, seizure type, diagnosis, degree of developmental delay, or specific EEG characteristics. Of note, 3 parameters came very close to significance but failed to attain it: children with more antiepileptic medications, children with diffuse slowing on EEG, as well as those with higher LDL cholesterol and higher cholesterol/HDL ratio were more likely to be successful. Conclusion: The results have the potential for directing KD therapy toward those who are most likely to succeed. The main limitation of this study is its small sample size. However, finding significant differences in such a small sample size is surprising, and if replicated in larger studies, can possibly transform patient selection for the KD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".