Efficacy of ketogenic diet therapy in infants with epilepsy: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: The ketogenic diet therapy, a high-fat, low-carbohydrate diet, has been known since the 1920s as a therapeutic option in treating drug-resistant epilepsy. However, with the increasing incidence of the infant population, research on this subject is still limited. This systematic review and meta-analysis aimed to evaluate the efficacy of ketogenic diet therapy in infants with epilepsy. Methods: We searched the articles from Cochrane Library, Embase, Pubmed, ScienceDirect, and Scopus, based on predetermined inclusion criteria. Four investigators independently performed screening, study selection, extracted data, and assessed the quality of relevant articles. We used the Newcastle-Ottawa Quality Assessment Scale to assess the risk of bias in included articles. We present the results of the meta-analysis using a forest plot. Results: We identified 1781 studies from database screening, with eight cohort studies in this study. Our meta-analysis revealed that an estimate of 69% of infants with epilepsy achieved ≥50% seizure reduction in three months follow-up (95% confidence interval [CI] 56- 82%) and an estimate of 36% of infants achieved seizure freedom (95% confidence interval [CI] 20- 51%). Retention rates ranged from 91% at three months to 28% at 24 months. The most common side effects reported were dyslipidemia (131/355, 36.9%), gastrointestinal disturbances (66/355, 18.6%), and hyperkeratosis/acidosis (42/355, 11.8%). Conclusion: Ketogenic diet therapy is well tolerated and effectively reduces seizure frequency at three months in infants with epilepsy.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".