Maintenance of response to ketogenic diet therapy for drug-resistant epilepsy post diet discontinuation: A multi-centre case note review
Bibliographic record
Abstract
PURPOSE: There is limited research on the proportion of individuals with epilepsy who maintain response to ketogenic diet therapy (KDT) after discontinuing treatment. We aimed to determine the proportion of individuals who did / did not maintain response post KDT and explore factors that may influence the likelihood of maintaining response. METHODS: Retrospective data were collected from 97 individuals from 9 KDT centres. Individuals had achieved ≥50 % seizure reduction on KDT for at least 12 months, with seizure frequency data available at 3 months+ post diet. Outcome 1 was: recurrence of seizures or increase in seizure frequency post diet; outcome 2: recurrence of seizures, increase in seizure frequency or an additional anti-seizure treatment started post diet. RESULTS: 61/97 (62.9 %) individuals maintained response at latest follow-up (mean 2.5[2.0] years since stopping KDT). Approximately one third maintained response without further anti-seizure treatments. One quarter of individuals had an increase in frequency or recurrence of seizures within 6 months (95 %CI 4, 12) for outcome 1 and within 3 months (3, 6) for outcome 2. Individuals who did not achieve seizure freedom on diet were significantly more likely to have an increase in seizures or to require additional anti-seizure treatments post diet compared to those who were seizure-free on diet (hazard ratio 4.02, 95 %CI (1.46, 11.16) p < 0.01). CONCLUSION: Our findings should help guide clinical teams with the information they provide patients and their families regarding likelihood of long-term seizure response to KDT. Realistic costings for KDT services may need to be considered.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".