Bibliographic record
Abstract
Introduction Ketogenic Diet (KD) has been introduced as a treatment option for epilepsy over a century ago and since then it had gained and lost its popularity. Its current revival is heavily sensationalized and KD is promoted as a cure-it-all for a wide range of mental illnesses. The present review aims to review the validity of the evidence supporting KD use in psychiatry. Objectives 1. To review the origins and types of ketogenic diet as well as the possible rationale for its use in psychiatry 2. To provide a narrative review of effectiveness of ketogenic diet for management of major mental illnesses Methods Scoping review of research articles published in PubMed without restrictions in terms of language or publication date. Results We conducted a scoping review using a wide range of keywords. There are multiple studies dedicated fully or partially on ketogenic diet and a variety of mental illnesses. Majority of studies are focused on low-carbohydrate diets and not necessarily on KD per se, which complicates interpretation of findings. At the same time, we were able to identify multiple case studies and case series as well as some open-label and placebo-controlled studies involving small groups of participants receiving KD mostly as an adjunct treatment for their respective condition. There was a significant degree of heterogeneity both in terms of methodological aspects and clinical outcomes of these studies, which do not allow for further data synthesis at this point. At the same time, there are some indications that KD may be useful in certain clinical scenarios. Conclusions The objective data on impact of ketogenic diet on major mental illnesses are scarce, but show some promise. Further research is needed to elaborate both the mechanisms and the degree of impact of ketogenic diet on mental health. There is not enough evidence to suggest that ketogenic diet is an independent treatment option for any psychiatric condition, but at the same time it can be suggested as an adjunct measure. Disclosure of Interest None Declared
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".