Combined Cancer Patient–Reported Symptom and Health Utility Tool for Routine Clinical Implementation: A Real-World Comparison of the ESAS and EQ-5D in Multiple Cancer Sites
Bibliographic record
Abstract
Background: We assessed whether the presence and severity of common cancer symptoms are associated with the health utility score (HUS) generated from the EQ-5D (EuroQol Research Foundation, Rotterdam, Netherlands) in patients with cancer and evaluated whether it is possible pragmatically to integrate routine hus and symptom evaluation in our cancer population. Methods: Adult outpatients at Princess Margaret Cancer Centre with any cancer were surveyed cross-sectionally using the Edmonton Symptom Assessment System (ESAS) and the EQ-5D-3L, and results were compared using Spearman correlation coefficients and regression analyses. Results: Of 764 patients analyzed, 27% had incurable disease. We observed mild-to-moderate correlations between each ESAS symptom score and the HUS (Spearman coefficients: −0.204 to −0.416; p < 0.0001 for each comparison), with the strongest associations being those for pain (R = −0.416), tiredness (R = −0.387), and depression (R =−0.354). Multivariable analyses identified pain and depression as highly associated (both p < 0.0001) and tiredness as associated (p = 0.03) with the HUS. The ability of the ESAS to predict the HUS was low, at 0.25. However, by mapping ESAS pain, anxiety, and depression scores to the corresponding EQ-5D questions, we could derive the HUS using partial ESAS data, with Spearman correlations of 0.83–0.91 in comparisons with direct EQ-5D measurement of the HUS. Conclusions: The HUS derived from the EQ-5D-3L is associated with all major cancer symptoms as captured by the ESAS. The ESAS scores alone could not predict EQ-5D scores with high accuracy. However, ESAS-derived questions assessing the same domains as the EQ-5D-3L questions could be mapped to their corresponding EQ-5D questions to generate the HUS, with high correlation to the directly measured HUS. That finding suggests a potential approach to integrating routine symptom and HUS evaluations after confirmatory studies.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".