A modified Edmonton Symptom Assessment Scale for symptom clusters in radiation oncology patients
Bibliographic record
Abstract
Patient-reported outcomes regarding symptom burden may provide valuable information in addition to physician assessment. Systematic collection of patient-reported outcomes may be an important metric to identify unmet needs and improve quality of patient care. To understand common symptoms of patients seen in radiation oncology clinic, we examined the prospectively collected modified Edmonton Symptom Assessment Scale (ESAS-r) data to explore symptom clusters. Our clinic established use of a modified Edmonton Symptom Assessment Scale in August 2015. All outpatients presenting for radiation oncology services completed the form at each clinic visit. Symptom clusters are defined by two or more symptoms that are interrelated and occur simultaneously with a high degree of predictability. A sample of 916 de-identified surveys was assessed statistically using principal component analysis (PCA) with varimax rotation to determine independent clustering between the symptoms queried. We found four major clusters of symptoms: Tiredness (tired, drowsiness; PC1), Loss of Appetite (nausea, lack of appetite; PC2), Low Well-Being (overall & spiritual well-being; PC3), and Depression (depression, anxiety; PC4). These accounted for 46%, 9.2%, 7.6%, and 7% of total variance, respectively. Internal consistency using Cronbach's alpha was 0.87, 0.7, 0.82, and 0.87, respectively. The most frequent write-in item was itchiness, present in 24% of the 148 patients responding. Assessment of patients seen in a large radiation oncology clinic revealed several symptom clusters. {Tiredness and drowsiness} represents a major symptom cluster. Itchiness may be underrecognized.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".