The impact of n-3 polyunsaturated fatty acids in patients with cancer: emerging themes
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes recent literature falling broadly under the topic of n-3 polyunsaturated fatty acids (PUFAs) in the oncology setting, highlighting emerging themes and emphasizing novel explorations. RECENT FINDINGS: Meta-analyses continue to confirm safety and efficacy of n-3 PUFA supplementation on reducing inflammation and improving survival in people with cancer. Common themes in recent studies emphasize improving tumor-directed efficacy and reducing toxicities of common cancer therapies. New areas of interest include the impact of n-3 PUFA when combined with immunotherapies and applications in pediatric acute lymphoid leukemia. Novel assessments include specialized pro-resolving lipid mediators, the intestinal microbiome and psychological well being. A variety of clinically relevant outcomes including nutritional status, toxicities and survival are being explored in ongoing clinical studies. SUMMARY: Evidence confirms the safety of n-3 PUFA for patients with cancers, as well as benefits in some, but not all areas of exploration. Larger, well designed trials with biological assessment of compliance compared to the prescribed n-3 PUFA dose would strengthen the evidence needed to integrate n-3 PUFA recommendations into clinical practice for patients with cancer.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".