Bibliographic record
Abstract
Cancer cells' metabolic Warburg effect behavior, in which they rely predominantly on aerobic glycolysis, offers a promising opportunity for focused intervention in tumor therapy. Since nutrient oxidation is linked to this metabolic pathway, manipulating one's diet may be a useful method for dealing with tumors. The high-fat, low-carbohydrate composition of the ketogenic diet causes a metabolic condition called ketosis, which is similar to the states of fasting and starvation on the body. The body is coerced into using fat for fuel, which in turn produces ketone bodies that can be used as alternative metabolites in the creation of aerobic energy. Weight loss, epilepsy therapy, and management of diabetes are just a few of the health benefits that have contributed to the ketogenic diet's meteoric rise in popularity. New studies are pointing to the ketogenic diet's promise in the treatment of cancer as well as its more traditional uses. To better utilize the ketogenic diet as a novel approach to cancer treatment and to refine and optimize the existing dietary protocol through scientific inquiry, this paper examines the ketogenic diet's use from an energy perspective and its potential implications in cancer treatment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".