End-of-Life Nutrition Considerations: Attitudes, Beliefs, and Outcomes
Bibliographic record
Abstract
OBJECTIVE: To assess the physiological outcomes and interpersonal influences that should be considered when making the decision to provide artificial nutrition and hydration (AN&H) for patients in hospice/palliative programs. METHODS: A systematic review was conducted using items from the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. Distinct search strategies were employed to find primary research articles that addressed: General health outcomes of artificial nutrition and hydration interventions and nutrition therapy interventions (n = 16), nutrition-related symptoms in end-of-life care (n = 8), and the attitudes of patients and providers toward artificial nutrition and hydration (n = 21). RESULTS: The effect of AN&H on health outcomes, quality-of-life measures and nutrition-related symptoms is limited and may vary by patient setting and diagnosis. In the absence of consistent evidence for specific health outcomes, decisions regarding AN&H should be made in context of the desires and beliefs of a patient, their family, and their medical providers. These beliefs may not be consistent with likely outcomes or may be inconsistent between individuals involved in the decision-making process, and individuals of different cultures or geographic regions may approach AN&H decisions from different perspectives. To help navigate the intersection of nutrition-related health outcomes and patient/provider beliefs, palliative care teams may employ a variety of strategies for approaching the decision-making process, and may benefit from specific involvement of a Registered Dietitian to help contribute to or lead these discussions.
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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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".