Difficulties in Addressing Artificial Hydration and Nutrition Therapy for Terminal Cancer Patients: What to do if Patients/Families’ Wishes Differ From the Medically Appropriate Treatment Plans?
Bibliographic record
Abstract
Purpose: Artificial hydration and nutrition therapy (AHNT) initiated by patients/families sometimes differs from medically appropriate treatment plans. We aimed to identify the causes of these differences and examine the ensuing responses and outcomes. Methods: Of 133 adult cancer patients receiving end-of-life care in the last 2 years, these discrepancies occurred in 41 patients. We retrospectively examined the following issues: (1) The reason why these discrepancies occurred. (2) Based on the causes identified in (1), the following actions were taken: 1) If the consent to change to medically appropriate AHNT was obtained, physical findings using Japan Palliative Oncology Study (JPOS) group and Edmonton Symptom Assessment System (ESAS) were compared before and 1 week after the intervention. 2) If consent was not obtained, time-limited trial (TLT) was conducted, and these results were compared. (3) The communication between patients/families and medical professionals was compared using Support Team Assessment Schedule. Results. (1) Causes: a) the lack of understanding of the disease condition in 26 cases and b) faulty expectation of AHNT in 15 cases. (2) In 30 cases of 1) (20 of a) and 10 of b)) and 11 of 2) in which TLT was performed, JPOS and ESAS improved significantly. (3) The communication above was improved significantly in 1) and 2) ( P = .0027 and .0039, respectively). Conclusion. Providing medically appropriate AHNT with the consent of patients/families is expected to not only alleviate distressing symptoms but also improve the communication between patients/families and medical professionals, as well as improve the quality of palliative care.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".