Patients’ self-reported overall wellbeing correlates with concurrent reported symptoms: analysis of the Edmonton Symptom Assessment System
Bibliographic record
Abstract
Abstract Background: The primary intent of cancer treatment is either curative, prolongation of patient life, or to improve patient quality of life; however, treatments are associated with various side effects that may impact patient wellbeing. Thus, understanding the patients’ wellbeing from the patient’s perspective is essential as it could help enable the provision of the necessary support for patients throughout their cancer journey. Materials and Method: We analysed Edmonton Symptom Assessment System (ESAS) questionnaire responses completed by 19,288 patients over 201,839 visits to our Cancer Centre. As part of their routine and standard of care, patients completing the questionnaire are asked to score 6 physical and 2 psychological symptoms as well as overall wellbeing using an 11-point numerical rating scale ranging from 0 to 10, where 0 means complete absence of the symptom or best overall wellbeing and 10 means worst possible symptom or worst overall wellbeing. We used the ESAS responses to characterise the relationship between the overall wellbeing score and concurrent symptoms scored by cancer patients. Results: Patients reported tiredness and nausea as the physical symptom causing the most and least distress respectively. Patients that reported severe (7–10) wellbeing also scored high mean scores for tiredness (6·2 ± 2·7), drowsiness (4·7 ± 3·1) and lack of appetite (4·4 ± 3·4). Univariate and multivariable logistic regression analysis suggests higher odds for patients to report moderate-to-severe (4–10) wellbeing when they report moderate-to-severe concurrent symptoms compared to none-to-mild concurrent symptoms. Conclusions: Our findings suggest that patients’ overall wellbeing as reported by the ESAS system is influenced by a number of concurrent symptoms. Tiredness was found to impact patients’ overall wellbeing to a greater extent than other concurrent symptoms. The sum of physical or psychological symptom scores was stronger indicators of a patient’s overall wellbeing compared to the scores of individual symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".