Symptom Clusters Using Edmonton Symptom Assessment System in Radiotherapy and Palliative Care Outpatient Clinic
Bibliographic record
Abstract
Background/Objectives: Effective palliative care relies on accurate identification and management of symptoms, especially in patients referred for palliative radiotherapy (PRT). This study aimed to identify symptom clusters (SCs)—defined as ≥2 interrelated symptoms—in patients evaluated at a multidisciplinary Radiotherapy and Palliative Care (RaP) outpatient clinic, using the Edmonton Symptom Assessment System (ESAS). Methods: We retrospectively analyzed data from patients referred to the RaP clinic between February 2017 and April 2020. Demographic and clinical characteristics, including ESAS scores at first visit, were collected. Principal component analysis (PCA) and unsupervised K-means clustering (KMC) were used to identify SCs. Associations with ECOG performance status (PS), primary tumor site, metastases site and PRT administration were analyzed. Exploratory survival analyses were performed.Results: Among 215 patients (median age 71 years; 53% male), the mean total ESAS score was 24.03 ± 15.28. PCA identified four SCs: SCPCA1 (tiredness, drowsiness, dyspnea, malaise), SCPCA2 (depression, anxiety), SCPCA3 (nausea, loss of appetite) and SCPCA4 (pain). KMC revealed three SCs: SCKMC1 (pain, tiredness, drowsiness, malaise), SCKMC2 (nausea, loss of appetite, dyspnea), SCKMC3 (depression, anxiety). Worse ECOG PS correlated significantly with physical SCs (p < 0.05). A trend linked SCKMC1 with greater PRT use. Psychological SCs (SCPCA2, SCKMC3) were significantly associated with a lower likelihood of receiving PRT. A trend toward shorter survival was observed among patients belonging to SCKMC2. Conclusions: SC analysis could improve clinical decision-making in the PRT setting. SC profiles reflect patient complexity and may guide personalized symptom management and treatment selection in advanced cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".