Assessment and Management Tools for Advancing Disease
Bibliographic record
Abstract
As the population of the United States has aged, the number of home care patients with multiple chronic diseases has also increased. A review of the literature suggested that layering palliative care principles on traditional home healthcare could improve outcomes for patients with advancing disease. The purpose of this quality improvement project was to educate home care staff on the use of assessment and management tools to identify symptoms and provide symptom control, prevent unnecessary hospitalizations, and reduce healthcare costs in patients with advancing disease. This project took place at eight offices of a national home care agency in Kentucky and Indiana. Nurses, social workers, and therapists attended mandatory education sessions over 5 months on integrating palliative care principles into care planning for home care patients and using the Edmonton Symptom Assessment Scale (ESAS) and state-specific end-of-life planning tools. Symptoms were tracked and managed. A paired-samples t-test was used to analyze ESAS scores. Hospitalization and rehospitalization rates and costs were compared and benchmarked. Some symptoms worsened from beginning to end of the episode of care, suggesting a need for additional staff training about symptom management. The wide range of symptoms and symptom worsening suggest the need for individualized care, possibly extended home healthcare, and the inclusion of a more formal palliative care course for staff on symptom management.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".