Parenteral Nutrition in Palliative Cancer Care: Detrimental, Futile, or Beneficial?
Bibliographic record
Abstract
Palliative cancer care patients may live for a long time, but malnutrition worsens the prognosis. Parenteral nutrition (PN) is suitable for replenishing a calorie deficit, but its advantages and tolerance late in the cancer trajectory are debated. We examined symptom development in hospitalized patients with and without PN. A total of 21 palliative cancer care patients receiving PN and 155 palliative cancer care patients not receiving PN during hospitalization in a specialized unit were retrospectively compared. We studied symptom intensity at admission, symptom relief during the hospital stay, and survival. The patients had locally advanced or metastatic cancer, a mean age of 70 years, and their median ECOG performance status was III. Symptom burden at admission was similar in the compared groups. Symptom relief during hospitalization was also similar. However, patients already on PN at admission reported more nausea and patients receiving PN during hospitalization reported better nausea relief compared to patients not receiving this intervention. Overall median survival was less than two months and similar in the compared groups. Based on a limited number of observations and a suboptimal study design, we were not able to demonstrate an increased symptom burden for palliative cancer care patients receiving PN late in the disease trajectory.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".