Neutropenia in Children Treated With Ketogenic Diet Therapy
Bibliographic record
Abstract
OBJECTIVES: The objectives were to investigate the relationship between ketogenic diet therapy and neutropenia in children with epilepsy. METHODS: A retrospective chart review of children who initiated ketogenic diet at the Hospital for Sick Children between January 1, 2000, and May 1, 2018 was performed. Factors associated with the development of neutropenia during ketogenic diet therapy were evaluated and the relationship between development of a significant or suspected infection and neutrophil count was analyzed. RESULTS: One hundred two children met inclusion criteria and were followed on the diet for up to 24 months. Thirteen of 102 (13%) children were neutropenic at diet initiation. In the remaining 89 children, 27 developed neutropenia. Developing neutropenia was significantly associated with the ketogenic diet at 6 (13%), 12 (23%), and 24 (25%) months follow-up. Developing neutropenia was associated with higher urinary ketones (OR = 4.26, 95% CI: 1.27, 14.15) and longer duration of ketogenic diet therapy (OR = 3.29, 95% CI: 1.42, 7.96). There was no significant association between development of a clinically significant infection and neutropenia. CONCLUSION: Ketogenic diet therapy is associated with neutropenia in children with epilepsy, however, it does not have a significant clinical impact. Concern regarding neutropenia should not discourage the use of the ketogenic diet in children.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".