Home parenteral nutrition in advanced cancer: where are we?
Bibliographic record
Abstract
Patients with advanced and incurable cancer are a compelling group. Questions and comments that these individuals and their families have may include: "My daughter is expecting our first grandchild in 3 months--can I hope to see our new family member?"; "I can't keep any food down--is there anything I can do?"; "I am worried about losing so much weight, and feeling tired and weak--is there anything that may help?"; "Will I suffer a lot?". Indeed, the most pressing concerns of the patient relate to predictions about survival and control of symptoms. The clinician taking care of the patient may wonder what is the utility or futility of home parenteral nutrition (HPN) in both the individual with advanced cancer and in this population of patients at large, whether there is potential for harm such as increasing the burden of care or prolonging suffering, and how to optimize care and communication with the patient and their families. The nutrition scientist may want to know what the implications of advanced cancer are on nutrient requirements and utilization, whether there are markers that would differentiate between cachexia and simple starvation, and whether it is possible to use specific nutrients to modify the disease process. This review will provide insights into the understanding of the role of HPN in advanced cancer and opportunities for further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".