Symptom Burden in the First Year After Cancer Diagnosis: An Analysis of Patient-Reported Outcomes
Bibliographic record
Abstract
Purpose Improvement in the quality of life of patients with cancer requires attention to symptom burden across the continuum of care, with the use of patient-reported outcomes key to achieving optimal care. Yet there have been few studies that have examined symptoms in the early postdiagnosis period during which suboptimal symptom control may be common. A comprehensive analysis of temporal trends and risk factors for symptom burden in newly diagnosed patients with cancer is essential to guide supportive care strategies. Methods A retrospective observational study was performed of patients who were diagnosed with cancer between January 2007 and December 2014 and who survived at least 1 year. Patient-reported Edmonton Symptom Assessment System scores, which are prospectively collected at outpatient visits, were linked to provincial administrative health care data. We described the proportion of patients who reported moderate-to-severe symptom scores by month during the first year after diagnosis according to disease site. Multivariable logistic regression models were constructed to identify risk factors for moderate-to-severe symptom scores. Results Of 120,745 patients, 729,861 symptom assessments were recorded within 12 months of diagnosis. For most symptoms, odds of elevated scores were highest in the first month, whereas nausea had increased odds of elevated scores up to 6 months after diagnosis. On multivariable analysis, cancer site, younger age, higher comorbidity, female sex, lower income, and urban residence were associated with significantly higher odds of elevated symptom burden. Conclusion A high prevalence of moderate-to-severe symptom scores was observed in cancers of all sites. Patients are at risk of experiencing multiple symptoms in the immediate postdiagnosis period, which underscores the need to address supportive care requirements early in the cancer journey. Patient subgroups who are at higher risk of experiencing moderate-to-severe symptoms should be targeted for tailored supportive care interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".