The ketogenic diet as a treatment for traumatic brain injury: a scoping review
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a leading cause of morbidity and mortality worldwide. The ketogenic diet (KD) has been identified as a potential therapy to enhance recovery after TBI. The purpose of this study is to complete a scoping review and synthesize the evidence regarding the KD and its therapeutic effects in TBI. The methodological framework of Arksey and O'Malley was employed. Databases searched include Medline, EMBASE, CCRCT, CINAHL and WebOfScience. Two reviewers independently screened titles, abstracts and full texts in a two-step screening protocol to determine inclusion. Abstracted data included study setting and therapeutic mechanism. The KD was demonstrated to reduce cerebral oedema, apoptosis, improve cerebral metabolism and behavioural outcomes in rodent TBIs. Additionally, the KD affected rodent TBIs in an age-dependent manner. Due to a lack of relevant outcome measures, the human trials did not establish much evidence with respect to the KD as a treatment for TBI; only its safety was established. The KD is an effective treatment for TBI recovery in rats and shows potential in humans. Future research should aim to better elucidate the KD's mechanisms of action in human TBIs and determine if the KD's effectiveness on clinical outcomes can be reproduced in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".