Increase of Human Milk Fat Inducing Nutritional Ketosis in Exclusively Breastfed Infant, Brought About by Treating the Mother With Ketogenic Dietary Therapy
Bibliographic record
Abstract
INTRODUCTION: Medicalized Ketogenic Therapy is commonly used to treat refractory epilepsy. Patients have varying degrees of seizure or symptom relief, responding at individual levels of ketone production. Typically, initiating the therapy necessitates the discontinuation of breastfeeding. Our case study mother was keen to continue breastfeeding if possible. We were able to achieve this by placing the healthy mother on a ketogenic diet and altering the composition of the mother's own milk. MAIN LACTATION ISSUE: Pediatric Medicalized Ketogenic Therapy is delivered through a ketogenic diet consisting of up to 90% fat, measuring of ingredients to 0.1 g matching a food prescription of fat, protein, and carbohydrate. We placed the mother on a less stringent ketogenic diet achieving 61% fat and measured both infant and mother's blood sugar levels and ketones. The hypothesis was that changes would occur in the mother's own milk fat content, and/or ketones would be passed directly to the infant. If therapeutic levels of ketones were reached in the infant and a reduction in seizures observed, breastfeeding could continue. MANAGEMENT OVERVIEW: Over 3 months we achieved a calorific increase of the mother's mature milk by an additional 134%. The infant was successfully put into nutritional ketosis and visible seizures eliminated. CONCLUSION: Medicalized Ketogenic Therapy can be safely used to treat seizures of breastfeeding infants diagnosed with epilepsy, through management of the mother on a ketogenic diet. Significantly increasing the mature mothers own milk fat component could have implications for other areas, including faltering growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".