Symptom Burden and Shared Care Planning in an Oncology Nurse-Led Primary Palliative Care Intervention (CONNECT) for Patients with Advanced Cancer
Bibliographic record
Abstract
Purpose: Primary palliative care (PPC) interventions are needed to address unmet symptom needs within standard oncology care. We designed an oncology nurse-led PPC intervention using shared care planning to facilitate patient engagement. This analysis examines the prevalence and severity of symptoms reported by patients and how symptoms were addressed on shared care plans (SCPs). Methods: Secondary analysis of a cluster randomized PPC intervention trial. Adult patients with metastatic solid tumors whose oncologist “would not be surprised if the patient died within a year” were included. Twenty-three oncology nurses received PPC training and conducted up to three monthly visits with patients. Symptom prevalence and severity were assessed before each visit using the Edmonton Symptom Assessment Scale (ESAS). Nurses collaboratively developed treatment strategies with patients, targeting the most bothersome symptoms for improvement. Results: Among 571 nurse-led PPC visits with 235 patients, the most prevalent and severe symptoms were tiredness (reported at 86% of visits; ESAS ≥4 in 55% of visits), low sense of wellbeing (78%; ESAS ≥4 in 38%), and poor appetite (69%; ESAS ≥4 in 42%). Moderately severe symptoms were addressed on SCPs ranging from 4% (drowsiness) to 35% (tiredness) of the time. Symptom management plans developed by PPC-trained oncology nurses primarily focused on nonpharmaceutical interventions (70%) compared with pharmaceutical interventions (30%). Conclusion: The symptoms that patients report most frequently and as most severe on SCPs were addressed less frequently than expected. Further research is needed to understand how PPC interventions can be designed to more effectively target and improve bothersome symptoms for patients with advanced cancer. Clinical Trial Registration:ClinicalTrials.gov identifier: NCT02712229
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".