Bibliographic record
Abstract
Different diet-based approaches have been studied as potential adjuvants to standard cancer therapies in human clinical trials. However, these diets have been shown to have complications such as non-compliance and adverse side effects. This paper investigates four different types of diet-based approaches used in human clinical trials and compares their complications. The four diet-based approaches evaluated in this paper are ketogenic diet (KD), protein restriction, fasting and fasting mimicking diets (FMD), and combined interventions. Research shows that KDs have large reports of non-compliance from subjects, with subjects also experiencing significant weight loss, constipation, and fatigue. Protein restriction diets have greater levels of adherence from subjects but may lead to harmful hematological toxicities. Fasting and FMD showed greater adherence than subjects on KDs, and lower toxicities than subjects on protein restriction diets, but had a greater number of complaints of headaches, hunger, and dizziness. Finally, combined interventions have the fewest reports of side effects and non-compliance but suffer from a limited number of case studies. Given these results, diet-based interventions require further research to minimize side effects and non-compliance before becoming an accepted adjuvant to standard cancer therapy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".