Bibliographic record
Abstract
Objective: Many infancy-onset epilepsies are poorly responsive to anti-seizure medicines (ASMs) with poor prognosis for neurodevelopmental outcome.Ketogenic diets (KD) are high-fat, low-carbohydrate diets, shown to reduce seizures in older children and adults.No high-quality evidence is available for infants.Methods: Infants (age 1-24 months) with epilepsy, average ≥4 seizures/week and previous trial ≥2 ASMs, were randomised to receive a classical KD or further ASM.The primary outcome was difference in number of seizures during weeks 6-8 accounting for baseline.Results: Seventy-eight children were randomised to KD and 58 to ASM.The median number of daily seizures was similar in both groups at 8 weeks (IRR 1.33 95% CI 0.84, 2.11).The odds ratio of achieving ≥50% seizure reduction was 1.21 (95% CI 0.55, 2.65), and 0.88 (0.27, 2.80) for seizure freedom.A higher proportion of infants in the ASM group changed the number or dose of concurrent ASMs during the intervention period (24/48 [50%]) compared to KD (9/66 [14%]).Side effect score at 8 weeks was similar in both groups (KD median 40 IQR 38, 42; ASM median 41 IQR 39, 44).Overall health was numerically higher in the KD group (median 60 IQR 30, 60) at 8 weeks compared to ASM (median 30 IQR 30, 60).Communication (2.79 95% CI -8.14, 13.72) and socialisation (1.12 95% CI -17.13, 19.36) numerically improved in the KD group compared to ASM at 12 months.A similar proportion of infants in both groups reported at least one serious adverse event (43% ASM; 51% KD)-most commonly seizures.Conclusions: KD appears numerically similar in efficacy and tolerability to further ASM in infants with drugresistant epilepsy.The odds ratio of achieving seizure freedom at 8 weeks, and communication, socialisation and overall health scores numerically favoured KD compared to further ASM.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.808 | 0.632 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".