The utility of ketogenic diet therapy in the management of epileptic spasms: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We systematically reviewed the existing literature on the efficacy of the ketogenic diet (KD) in patients with epileptic spasms (ES) and analyzed predictors of seizure outcomes. METHODS: The Preferred Report Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed. The primary outcome was the proportion of patients achieving 50-99 % spasm reduction at 3 months following KD initiation. Additional outcomes included the proportion of patients achieving complete spasm freedom at 1, 3, and 6 months. Meta-regression was employed to examine clinical determinants of seizure outcomes. RESULTS: Twenty-two studies (n = 1077 individuals) were included. There were 607 males (62.2 %) and 369 females (37.8 %) (k = 16 studies). The mean age of KD initiation was 16 months (SD: 5.4, range: 8.4-23 months) (k = 11 studies). Epilepsy etiology was reported in 453 individuals (k = 15 studies), 297 (65.6 %) had a known etiology and 156 (34.4 %) were unknown. Structural (n = 178, 59.9 %) and genetic/metabolic (n = 80, 26.9 %) causes were common. Most received the classic ketogenic diet (n = 975/1077, 90.5 %) and 102 patients (9.5 %) received the modified Atkins diet (MAD). At 3 months, 48 % (95 % CI: 40 %, 57 %) achieved a 50-99 % spasm reduction and 25 % (95 % CI: 17 %, 34 %), achieved spasm freedom. At 6 months, 41 % (95 % CI: 32 %, 50 %) achieved 50-99 % spasm reduction and 25 % (95 % CI: 17 %,35 %), achieved spasm freedom. Meta-regression did not identify predictors of treatment response. CONCLUSION: KD may be an effective treatment option for ES. However, predictors of clinical response were not identified, highlighting the need for larger studies to better understand them and long-term outcomes.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".