Depletion of essential fatty acids in muscle is associated with shorter survival of cancer patients undergoing surgery-preliminary report
Bibliographic record
Abstract
Emerging studies are reporting associations between skeletal muscle abnormalities and survival in cancer patients. Cancer prognosis is associated with depletion of essential fatty acids in erythrocytes and plasma in humans. However the relationship between skeletal muscle membrane fatty acid composition and survival is unknown. This study investigates the relationship between fatty acid content of phospholipids in skeletal muscle and survival in cancer patients. Rectus abdominis biopsies were collected during cancer surgery from 35 patients diagnosed with cancer. Thin-layer and gas chromatography were used for quantification of phospholipid fatty acids. Cutpoints for survival were defined using optimal stratification. Median survival was between 450 and 500 days when patients had arachidonic acid (AA) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in muscle phospholipid below the cut-point compared to 720-800 days for patients above. Cox regression analysis revealed that low amounts of AA, EPA and DHA are risk factors for death. The risk of death remained significant for AA [HR 3.5 (1.11-10.87), p = 0.03], EPA [HR 3.92 (1.1-14.0), p = 0.04] and DHA [HR 4.08 (1.1-14.6), p = 0.03] when adjusted for sex. Lower amounts of essential fatty acids in skeletal muscle membrane is a predictor of survival in cancer patients. These results warrant investigation to restore bioactive fatty acids in people with cancer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".