Comparing the physical, psychological, social, and spiritual needs of patients with non-cancer and cancer diagnoses in a tertiary palliative care setting
Bibliographic record
Abstract
OBJECTIVE: The purpose was to describe the physical, psychological, social, and spiritual needs of patients with non-cancer serious illness diagnoses compared to those of patients with cancer. METHOD: We conducted a retrospective chart review of all patients with a non-cancer diagnosis admitted to a tertiary palliative care unit between January 2008 and December 2017 and compared their needs to those of a matched cohort of patients with cancer diagnoses. The prevalence of needs within the following four main concerns was recorded and the data analyzed using descriptive statistics and content analysis: •Physical: pain, dyspnea, fatigue, anorexia, edema, and delirium•Psychological: depression, anxiety, prognosis, and dignity•Social: caregiver burden, isolation, and financial•Spiritual: spiritual distress. RESULTS: The prevalence of the four main concerns was similar among patients with non-cancer and cancer diagnoses. Pain, nausea/vomiting, fatigue, and anorexia were more prevalent among patients with cancer. Dyspnea was more commonly the primary concern in patients with non-cancer diagnoses (39%), who also had a higher prevalence of anxiety and concerns about dignity. Spirituality was addressed more often in patients with cancer. SIGNIFICANCE OF RESULTS: The majority of patients admitted to tertiary palliative care settings have historically been those with cancer. The tertiary palliative care needs of patients with non-cancer diagnoses have not been well described, despite the increasing prevalence of this population. Our description of the palliative care needs of patients with non-cancer diagnoses will help guide future palliative care for the increasing population of patients with non-cancer serious illness diagnoses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".