Nutritional intervention with fish oil provides a benefit over standard of care for weight and skeletal muscle mass in patients with nonsmall cell lung cancer receiving chemotherapy
Bibliographic record
Abstract
BACKGROUND: Involuntary weight loss is a major contributor to mortality and morbidity in patients with advanced cancer. Nutritional intervention with fish oil (FO)-derived eicosapentaenoic acid (EPA) may prevent deterioration of body composition. This study compared intervention with FO with standard of care (SOC; no intervention) with regard to weight, skeletal muscle, and adipose tissue in newly referred patients with nonsmall cell lung cancer from the time of initiation to completion of first-line chemotherapy. METHODS: Forty patients completed the study; there were 16 in the FO group (dose of 2.2 g of EPA/day) and 24 patients in the SOC group. Skeletal muscle and adipose tissue were measured using computed tomography images. Blood was collected and weight was recorded at baseline and throughout chemotherapy. RESULTS: Patients in the SOC group experienced an average weight loss of 2.3 ± 0.9 kg whereas patients receiving FO maintained their weight (0.5 ± 1.0 kg) (P = .05). Patients with the greatest increase in plasma EPA concentration after FO supplementation were found to have the greatest gains in muscle (r(2) = 0.55; P = .01). Approximately 69% of patients in the FO group gained or maintained muscle mass. Comparatively, only 29% of patients in the SOC group maintained muscle mass, and overall the SOC group lost 1 kg of muscle. No difference in total adipose tissue was observed between the 2 groups. CONCLUSIONS: Nutritional intervention with 2.2 g of FO per day appears to provide a benefit over SOC, resulting in the maintenance of weight and muscle mass during chemotherapy.
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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) | 0.001 | 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".