Functional Impairment, Symptom Burden, and Clinical Outcomes Among Hospitalized Patients With Advanced Cancer
Bibliographic record
Abstract
BACKGROUND: National guidelines recommend regular measurement of functional status among patients with cancer, particularly those who are elderly or high-risk, but little is known about how functional status relates to clinical outcomes among hospitalized patients with advanced cancer. The goal of this study was to investigate how functional impairment is associated with symptom burden and healthcare utilization and clinical outcomes. PATIENTS AND METHODS: We conducted a prospective observational study of patients with advanced cancer with unplanned hospitalizations at Massachusetts General Hospital from September 2014 through March 2016. Upon admission, nurses assessed patients' activities of daily living (ADLs; mobility, feeding, bathing, dressing, and grooming). Patients with any ADL impairment on admission were classified as having functional impairment. We used the revised Edmonton Symptom Assessment System (ESAS-r) and Patient Health Questionnaire-4 to assess physical and psychological symptoms, respectively. Multivariable regression models were used to assess the relationships between functional impairment, hospital length of stay, and survival. RESULTS: Among 971 patients, 390 (40.2%) had functional impairment. Those with functional impairment were older (mean age, 67.18 vs 60.81 years; P<.001) and had a higher physical symptom burden (mean ESAS physical score, 35.29 vs 30.85; P<.001) compared with those with no functional impairment. They were also more likely to report moderate-to-severe pain (74.9% vs 63.1%; P<.001) and symptoms of depression (38.3% vs 23.6%; P<.001) and anxiety (35.9% vs 22.4%; P<.001). Functional impairment was associated with longer hospital length of stay (β = 1.29; P<.001) and worse survival (hazard ratio, 1.73; P<.001). CONCLUSIONS: Hospitalized patients with advanced cancer who had functional impairment experienced a significantly higher symptom burden and worse clinical outcomes compared with those without functional impairment. These findings provide evidence supporting the routine assessment of functional status on hospital admission and using this to inform discharge planning, discussions about prognosis, and the development of interventions addressing patients' symptoms and physical function.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".