Consistency of symptom clusters among advanced cancer patients seen at an outpatient supportive care clinic in a tertiary cancer center
Bibliographic record
Abstract
OBJECTIVE: Advanced cancer patients often develop severe physical and psychological symptom clusters (SCs), but limited data exist on their consistency or severity after an outpatient interdisciplinary team consultation led by palliative care specialists. The primary aim of the study was to determine the consistency and severity of SCs in advanced cancer patients in this setting. METHOD: A total of 1373 patients with advanced cancer who were referred to The University of Texas MD Anderson Cancer Center's Outpatient Supportive Care Center between January 2003 and October 2008 with a complete Edmonton Symptom Assessment Scale (ESAS; 0-10 scale) occurred at initial and first follow-up visit were reviewed (median 14 days, range 1-4 weeks). We used a Wilcoxon signed-rank test to determine whether symptoms changed over time, and a principal components factor analysis with varimax rotation to determine SCs at baseline and at first follow-up. The number of factors calculated was determined based upon the number of eigenvalues. RESULTS: The patients' ratings of the following symptoms (mean, SD) at the initial and follow-up visits, respectively, were: fatigue 6.2 (2.3) and 5.7 (2.5, p < 0.0001), pain 5.4 (2.9) and 4.6 (3, p < 0.0001), nausea 2.2 (2.8) and 2.0 (2.6, p < 0.0001), depression 3.0 (2.9) and 2.5 (2.7, p < 0.0001), anxiety 3.4 (3.0) and 2.8 (2.8, p < 0.0001), drowsiness 4.8 (3.1) and 4.4 (3.1, p < 0.0001), dyspnea 3.0 (2.9) and 2.7 (2.8), p < 0.0001), loss of appetite 4.2 (2.7) and 3.9 (2.7, p < 0.0001), sleep disturbances 4.2 (2.6) and 3.8 (2.6, P < 0.0001), and well-being 4.3 (2.5) and 3.9 (2.3, p < 0.0001). Cluster composition differentiated into physical (fatigue, pain, nausea, drowsiness, dyspnea, and loss of appetite) and psychological (anxiety and depression) components at the initial visit, and these two SCs were consistent upon follow-up. SIGNIFICANCE OF RESULTS: We conclude that SCs remain constant between baseline and near-term follow-up but that the severity of those symptoms lessened during that interval. This knowledge may allow palliative care teams to provide more targeted and higher-quality care, but further studies are needed.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".