Predictors Of 24-Hour Mortality After Transfer To The Acute Palliative Care Unit (APCU)
Bibliographic record
Abstract
Abstract:Context: Although patients are assessed by the palliative care consultation team before admission to the acute palliative care unit (APCU), patients may die within 24 hours of admission, causing distress to clinicians and the patientu2019s family. Objectives: To assess the risk factors associated with sudden death.Methods: A retrospective study of medical records of patients transferred to the APCU from October 1, 2013 to October 1, 2017 was performed to identify those who died within 24 hours after transfer. Characteristics of these patients were compared to a control group composed of a random sample of patients that were alive 24 hours after transfer to the APCU. Results: Seventy-nine patients died within 24 hours after transfer to the APCU. Compared to the control group, the patients that died within 24 hours were more likely to have a higher Memorial Delirium Assessment Scale (MDAS) score (P = 0.0001), a hematologic malignancy (P = 0.0002), to be transferred during weekdays (P = 0.0002), to be unresponsive (P = 0.0023), to be on oxygen therapy (P = 0.0395), and to have a higher Eastern Cooperative Oncology Group (ECOG) performance status (P = 0.0275). There was no significant difference in Edmonton Symptom Assessment System (ESAS) scores between groups.Conclusions: Patients with delirium, oxygen requirements, hematologic malignancies, low performance status, and unresponsiveness at the time of transfer are more likely to die within 24 hours of APCU transfer. A more thorough pre-transfer evaluation of these patients is required to minimize distress to patients, families, and staff.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".