Bibliographic record
Abstract
e24056 Background: Palliative care is an important intervention in helping alleviate symptom burden and decreasing suffering in patients facing serious illness. Additionally, early implementation of palliative care has shown to improve patient outcomes and quality of life. We aim to assess the impact of a post-discharge palliative care clinic for advanced cancer patients in decreasing symptom burden, increasing early hospice transition, improving quality of life (QOL), and decreasing 30-day hospital readmission. Methods: A post-discharge palliative care pilot clinic was created at the Stony Brook University Hospital (SBUH) cancer center. Data is collected, including patient symptom scores, utilizing the Edmonton Symptom Assessment Scale (ESAS), hospice transition, emergency department (ED) visits, and 30-day hospital readmission after the outpatient intervention. Additionally, a QOL assessment survey is being utilized to evaluate patient perspective on QOL after palliative care interventions at various time points. Results: After the first palliative care clinic visit post-hospital discharge there was improvement of several symptoms, most notably found in pain symptoms, with approximately one fourth of patients (11 of 41 followed for pain symptoms) having a significant reduction of their pain scores. Eighteen of forty six patients seen in the palliative care clinic transitioned to hospice, two patients visited the ED for various symptoms or complications related to their cancer, and only six patients had hospital readmissions within 30-days due to unavoidable factors. Conclusions: Our post discharge palliative care pilot clinic appears to have been successful in aiding in early hospice readmission, decreasing hospital readmission and improving symptom burden. Correlating this with QOL findings would provide additional insight and help develop further steps to support this patient population.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".