Nutritional support and quality of life in cancer patients undergoing palliative care
Bibliographic record
Abstract
In palliative care, the nutrition provided has to be tailored to the patient's needs, enhancing patient comfort and quality of life (QoL). We conducted a literature search to review methods of measuring QoL, and modalities of nutritional intervention and their influence on QoL of cancer patients in palliative care. Original papers published in English were selected from PubMed database by using the search terms, palliative medicine, cancer, nutrition and quality of life. Specific tools that are particularly recommended to assess QoL in a palliative care setting are reviewed. The main goal in palliative care is to maintain oral nutrition by providing nutritional counselling. Enteral nutritional support showed inconsistent effects on survival and QoL. An evidence-base for parenteral nutrition is still lacking. Ethical considerations concerning provision of food and hydration in end-of-life care are discussed. Nutritional status should be assessed early and regularly during treatment using appropriate tools. In the particularly acute context of palliative care, optimal patient management requires adequate education and counselling to patients and families. Meaningful interactions between the patient, caregivers and medical team would also increase the chance of resolving nutrition-related issues and help to fulfil each patient's specific nutritional needs and thus improve the QoL.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.002 | 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".