Characteristics of People with Dementia vs Other Conditions on Admission to Inpatient Palliative Care
Bibliographic record
Abstract
OBJECTIVES: Our aim was to (1) describe the clinical characteristics and symptoms of people diagnosed with dementia at the time of admission to inpatient palliative care; and (2) compare the nature and severity of these palliative care-related problems to patients with other chronic diseases. DESIGN: Descriptive study using assessment data on point of care outcomes (January 1, 2013, to December 31, 2018). SETTING: A total of 129 inpatient palliative care services participating in the Australian Palliative Care Outcomes Collaboration. PARTICIPANTS: A total of 29,971 patients with a primary diagnosis of dementia (n = 1,872), lung cancer (n = 19,499), cardiovascular disease (CVD, n = 5,079), stroke (n = 2,659), or motor neuron disease (MND, n = 862). MEASUREMENTS: This study reported the data collected at the time of admission to inpatient palliative care services including patients' self-rated levels of distress from seven common physical symptoms, clinician-rated symptom severity, functional dependency, and performance status. Other data analyzed included number of admissions, length of inpatient stay, and palliative care phases. RESULTS: At the time of admission to inpatient palliative care services, relative to patients with lung cancer, CVD, and MND, people with dementia presented with lower levels of distress from most symptoms (odds ratios [ORs] range from .15 to .80; P < .05 for all) but higher levels of functional impairment (ORs range from 3.02 to 8.62; P < .001 for all), and they needed more assistance with basic activities of daily living (ORs range from 3.83 to 12.24; P < .001 for all). The trends were mostly the opposite direction when compared with stroke patients. Patients with dementia tended to receive inpatient palliative care later than those with lung cancer and MND. CONCLUSION: The unique pattern of palliative care problems experienced by people with dementia, as well as the skills of the relevant health services, need to be considered when deciding on the best location of care for each individual. Access to appropriately trained palliative care clinicians is important for people with high levels of physical or psychological concerns, irrespective of the care setting or diagnosis. J Am Geriatr Soc 68:1825-1833, 2020.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".