Bibliographic record
Abstract
We appreciate the comments made by Drs. Likhodii and Burnham regarding our recent report demonstrating the anticonvulsant effects of acetoacetate and acetone in Frings audiogenic seizure-susceptible mice (1). They correctly point out that the anticonvulsant activity of acetone has been previously demonstrated or suggested in reports dating as far back as the 1930s. We are certainly not the first group to propose a causal relationship between acetone and the anticonvulsant efficacy of the ketogenic diet. Indeed, the most compelling mechanistic link between acetone and the anticonvulsant activity of the ketogenic diet (KD) was recently made in patients by using magnetic resonance spectroscopy (2). What remains unclear is how acetone actually exerts its anticonvulsant effects. Additionally, we do not know the metabolic interrelations among the three principal ketone bodies (β-hydroxybutyrate, acetoacetate, and acetone) that are relevant for seizure control. It should be recognized that within the relatively small community devoted to the study of KD mechanisms, Dr. Likhodii et al. (3) at the University of Toronto have performed to date the most extensive characterization of acetone's effects in acute animal seizure models, as well as in a kindling model. We look forward to reviewing their intriguing findings once these are published. Our results with the Frings mice reinforce their observation that acetone possesses a broad spectrum of anticonvulsant activity, and as such, further study of acetone's role in mediating the clinical efficacy of the KD is warranted. At a clinical level, the advent of reliable breath acetone testing (3) will likely provide an additional avenue to investigate the role of ketosis in the mechanism of KD action.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.322 | 0.188 |
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".