Patients’ characteristics and outcomes of palliative care inpatient consults at a comprehensive cancer center
Bibliographic record
Abstract
8524 Background: There is limited information about the characteristics and outcomes of inpatient palliative care consults in cancer centers. Two mobile teams (MT) each with a physician, fellow, and a nurse, provide consultation to hospitalized patients (pts) with complex symptoms. Methods: We analyzed the pts characteristics and outcomes during a two-month period. The charts were reviewed for demographics, cancer data, reason for consultation, symptoms, interventions, and outcomes. Results: Sixty-one pts were analyzed. Pain was the main reason for a consult request in 46 pts (75%), delirium in 10 (16%), anxiety in 4 (7%) and constipation in 5 (8 %). Some pts had more than one reason. 56(92%) pts had metastatic disease, diagnosis for ≤1 year in 26 (42%) and ≤2 years in 44 (72%) (Median =17 months). The Edmonton Symptom Assessment Scale (ESAS), 0–10, is summarized in the table. We uncovered new issues at consultation, such as 20 pts (38%) screened positive for alcoholism with CAGE questionnaire and the 34 pts (56%) had clinical delirium. Features of opioids toxicity such as constipation (N=43;70%), confusion (N=35;57%) (Mean MMSE = 23 ± 5), hallucinations (N=21;34%), myoclonus (N=16;26%) and miosis (N=18;29%) were frequently identified. The MT interventions included imaging studies (23%), enema (43%), laxatives (49%), neuroleptics (54%), metoclopramide (39%), corticosteroids (25%). Half (N=30;50%) of the pts had opioid rotation and/or had counseling (N=27;46%). One out of two pts (N=30;49%) required transfer to the palliative care unit. Conclusions: Most pts had previously undiagnosed opioid toxicity, delirium, and other symptoms. Opioid toxicity occurred secondary to rapid opioid escalation, possibly linked to chemical coping, and psychosocial distress. The outcome of these pts improved by opioid rotation, adding laxatives, metoclopramide, neuroleptics, and steroids. [Table: see text] No significant financial relationships to disclose.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".