Randomized trial of a symptom monitoring intervention for hospitalized patients with cancer.
Bibliographic record
Abstract
10005 Background: Hospitalized patients with cancer experience a high symptom burden, which is associated with poor health outcomes and increased healthcare utilization. We conducted a pilot randomized trial to assess the feasibility and preliminary efficacy of a symptom monitoring intervention to improve symptom management in hospitalized patients with advanced cancer. Methods: We randomly assigned patients with advanced cancer and unplanned hospitalizations who were admitted to the oncology service to a symptom monitoring intervention or usual care. Patients in both arms daily self-reported their symptoms (Edmonton Symptom Assessment System and Patient Health Questionnaire-4) via tablet computers. Patients assigned to the intervention had their symptom reports presented graphically with alerts for moderate/severe symptoms during daily team rounds. We defined the intervention as feasible if participants completed > 75% of their daily symptom assessments. We also observed daily team rounds to determine how often clinicians discussed and developed a plan to address patients’ symptoms. We used regression models to assess intervention effects on patients’ symptoms throughout their hospital stay and readmission risk. Results: From 10/26/16-6/30/17, we randomized 150 patients (81.1% enrollment rate; median age = 64.0 [22.7-92.8]; 40.7% female). The most common cancers were gastrointestinal (36.7%) and lung (22.0%). Patients completed 89.4% of their daily symptom assessments. Clinicians discussed 60.4% of the symptom reports and developed a plan during rounds to address patients’ symptoms 20.8% of the time. Compared with usual care, patients assigned to the intervention had a greater proportion of days with lower psychological distress (B = 0.12, P = .008). Intervention patients experienced improvements in their average symptom scores for drowsiness (B = -0.54, P = .033) and dyspnea (B = -0.43, P = 0.009). Intervention patients had lower risk of readmissions (hazard ratio = 0.68, P = .221), although this difference was not significant. Conclusions: This symptom monitoring intervention is feasible and demonstrates encouraging preliminary efficacy for improving patients’ symptoms and risk for readmissions. Clinical trial information: NCT02891993.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".