Post-discharge transitions of care for hospitalized patients with advanced cancer.
Bibliographic record
Abstract
6504 Background: Patients with advanced cancer experience frequent hospitalizations and burdensome transitions of care post-discharge. We examined predictors of discharge location for patients with advanced cancer. Methods: We prospectively enrolled patients with advanced cancer with unplanned hospitalizations from 9/14 to 3/16. Upon admission, we used the Edmonton Symptom Assessment Scale and Patient Health Questionnaire-4 to assess physical and psychological symptoms, respectively. We used logistic regression models to identify predictors of discharge to location other than home, including post-acute care (PAC) [skilled nursing facility or long term acute care hospital] or hospice [any setting]. We used Cox regression models adjusted for clinical variables to assess the relationship between discharge location and survival. Results: Out of 932 patients, 726 (77.9%) were discharged home, 118 (12.7%) to PAC and 88 (9.4%) to hospice. Compared with patients discharged home, those discharged to PAC or hospice had higher symptom burden, including dyspnea, constipation, low appetite, drowsiness, fatigue, depression, and anxiety (all p < 0.05). Patients discharged to PAC or hospice vs. home were more likely to be older (OR 1.03, p < 0.0001), live alone (OR 1.95, 95% CI: 1.25-3.02, p < 0.003), have impaired mobility (OR 5.08, 95% CI: 3.46-7.45, p < 0.0001), longer length of stay (OR 1.15, 95% CI: 1.11-1.20, p < 0.0001), higher ESAS physical symptoms (OR 1.02, 95% CI: 1.003-1.032, p < 0.017), and higher PHQ-2 depression symptoms (OR 1.13, 95% CI: 1.01-1.25, p < 0.027). Patients discharged to hospice vs. PAC were more likely to receive palliative care consultation (OR 4.44, 95% CI: 2.12 to 9.29, p < 0.0001) and have shorter length of stay (OR 0.84, 95% CI: 0.77 to 0.91, p < 0.0001). Compared with patients discharged home, those discharged to PAC had lower survival (HR 1.53, 95% CI 1.22-1.93, p < 0.0001). Conclusions: Patients with advanced cancer discharged to PAC or hospice have substantial physical and psychological symptom burden and poor physical function. Patients discharged to PAC also have inferior survival compared with those discharged home. They may benefit from targeted interventions to improve their quality of life and care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".