Symptom Clusters in Patients With Advanced Cancer: A Prospective Longitudinal Cohort Study to Examine Their Stability and Prognostic Significance
Bibliographic record
Abstract
This study's purpose was to assess symptom cluster (SC) stability during disease progression and determine their strength of association with survival in patients with advanced cancer . Consecutively eligible patients with advanced cancer not receiving cancer-specific treatment and referred to a Tertiary Palliative Care Clinic were enrolled in a prospective cohort study. At first consultation (D0) and in subsequent consultations at day 15 (D15) and day 30 (D30), patients rated 9 symptoms through the Edmonton Symptom Assessment System scale (0-10) and 10 others using a Likert scale (1-5). Principal components factor analysis with varimax rotation was used to determine SCs at each consultation. Of 318 patients with advanced cancer, 301 met eligibility criteria with a median age of 69 years (range 37-94). Three SCs were identified: neuro-psycho-metabolic (NPM), gastrointestinal, and sleep impairment, with some variations in their constitution over time. Exploratory factor analysis accounted for 40% of variance of observed variables in all SCs. Shorter median survival was observed continuously for NPM cluster (D0 23 vs. 58 days, P < .001; D15 41 vs. 104 days, P=.004; D30 46 vs. 114 days, P = .002), although the presence of 2 or more SCs on D0 and D15 also had prognostic significance (D0: 21 vs. 45 days, P = .005; D30: 50 vs. 96 days, P = .040). In a multivariable model, NPM cluster (D0 hazard ratio estimate: HR 1.64; 95%CI, 1.17-2.31; P = .005; D15 HR: 2.51; 95%CI, 1.25-5.05; P = .009; D30 HR: 3.9; 95%CI, 1.54-9.86; P = .004) and hospitalization (D0 HR: 2.27; 95%CI, 1.47-3.51; P < .001; D15 HR: 2.43; 95%CI, 1.18-5.01; P = .016; D30 HR: 3.41; 95%CI, 1.35-8.62; P = .009) were independently and significantly associated with worse survival. Three clinically relevant SCs were identified, and their constitution had small variations, maintaining a stable set of nuclear symptoms through disease progression. Presence of the NPM cluster and hospitalization maintained their prognostic value over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".