Reduction in Symptoms for Homebound Patients Receiving Home-Based Primary and Palliative Care
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of patients are living with multiple, chronic medical conditions and functional impairments that leave them homebound. Home-based primary and palliative care (HBPC) programs provide access to health care services for this vulnerable population. Homebound patients have high symptom burden upon program enrollment. Yet little is known as to how individual symptoms are managed at home, especially over longer time periods. OBJECTIVES: The purpose of this study was to determine whether high symptom burden decreases following HBPC enrollment. METHODS: All patients newly enrolled in an HBPC program who reported at least one symptom on the Edmonton Symptom Assessment Scale (ESAS) were eligible for telephone ESAS follow-up. Patients received a comprehensive initial home visit and assessment by a physician with subsequent follow-up care, interdisciplinary care management including social work, and urgent in-home care as necessary. Multivariate linear mixed models with repeated measures were used to assess the impact of HBPC on pain, depression, anxiety, tiredness, and loss of appetite among patients with moderate to severe symptom levels at baseline. RESULTS: One hundred forty patients were followed. Patient pain, anxiety, depression, and tiredness significantly decreased following intervention with symptom reductions seen at 3 weeks and maintained at 12 weeks. (p<0.01) Loss of appetite trended toward an overall significant decrease and showed significant reductions at 12 week follow-up. CONCLUSION: In a chronically ill population of urban homebound, patient symptoms can be successfully managed in the home. Future work should continue to explore symptom assessment and management over time for the chronically ill homebound.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".