Factors Influencing Clinical and Setting Pathways After Discharge From an Acute Palliative/Supportive Care Unit
Bibliographic record
Abstract
AIM: The aim of this study was to assess the factors which influence the care pathway after discharge from an acute palliative supportive care unit (APSCU). METHODS: Patients' demographics, indications for admission, kind of admission, the presence of a caregiver, awareness of prognosis, data on anticancer treatments in the last 30 days, ongoing treatment (on/off or uncertain), the previous care setting, analgesic consumption, and duration of admission were recorded. The Edmonton Symptom Assessment Scale (ESAS) at admission and at time of discharge (or the day before death), CAGE (cut down, annoy, guilt, eye-opener), and the Memorial Delirium Assessment Scale (MDAS), were used. At time of discharge, the subsequent referral to other care settings (death, home, home care, hospice, oncology), and the pathway of oncologic treatment were reconsidered (on/off, uncertain). RESULTS: A total of 314 consecutive cancer patients admitted to the APSCU were surveyed. Factors independently associated with on-therapy were the lack of a caregiver, home discharge, and short hospital admission, in comparison with off-treatment, and less admission for other symptoms, shorter hospital admission, discharge at home, and better well-being, when compared with "uncertain." Similarly, many factors were associated with discharge setting, but the only factor independently associated with discharge home was being "on-therapy." CONCLUSIONS: The finding of this study is consistent with an appropriate selection of patients after being discharged by an APSCU, that works as a bridge between active treatments and supportive/palliative care, according the concept of early and simultaneous care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".