Bibliographic record
Abstract
Scientists are trying to develop combination therapies for the treatment of malignant diseases, such as glioblastoma, that will increase the survival rate and reduce the side effects. Studies have shown that the efficacy of the classic treatment for glioblastoma, surgical resection combined with chemotherapy and radiation, will be greater if the metabolism of cancer cells is modified by nutritional habits. A ketogenic diet with caloric restriction is a key strategy in killing tumor cells by decreasing the amount of glucose as a source of energy, suppressing oxidative stress in tumor cells, inhibiting the signaling pathways of tumor markers, and reducing tumor angiogenesis and growth factors. Moreover, the application of a ketogenic diet and caloric restriction reduces the side effects of the chemotherapy and radiation used in the treatment of cancer cells. Another advantage of a ketogenic diet in the treatment of glioblastoma is the accessibility and affordability of changing nutritional habits for all socioeconomic classes. In vitro and in vivo studies have shown the efficacy of a ketogenic diet, but more clinical studies are needed. Animal studies have demonstrated that a ketogenic diet increased the performance and survival of mouse models with malignant glioma. There are currently some pilot studies underway in the United States and Germany that will be completed by 2018.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".