Impact of Standardized Edmonton Symptom Assessment System Use on Emergency Department Visits and Hospitalization: Results of a Population-Based Retrospective Matched Cohort Analysis
Bibliographic record
Abstract
PURPOSE: The Edmonton Symptom Assessment System (ESAS) is a validated instrument whose use has been standardized in the Ontario cancer system to measure symptoms among ambulatory patients with cancer. The objective was to examine the effect of ESAS exposure on visits to the emergency department (ED) and hospitalizations. METHODS: This was a retrospective matched cohort study conducted in Ontario, Canada. The study included patients ≥ 18 years of age diagnosed with cancer between 2007 and 2015. Patients were considered exposed if they were screened with ESAS at least once during the study period, and their first ESAS screening date was defined as the index date. Each exposed patient was matched randomly to a patient with cancer without ESAS assessment using a combination of hard matching (birth year ± 2 years, cancer diagnosis date ± 1 year, cancer type, and sex) and propensity score matching (14 variables, including cancer stage, treatments received, and comorbidities). A multivariable Andersen-Gill recurrent event model was used to evaluate the effect of ESAS on the rate of health care use. RESULTS: The analysis included 128,893 matched pairs that were well balanced on baseline measures. After adjusting for other variables, patients with ESAS had lower rates of both ED visits (relative rate [RR], 0.92; 95% CI, 0.91 to 0.93) and hospitalizations (RR, 0.86; 95% CI, 0.85 to 0.87) compared with patients without ESAS. CONCLUSION: ESAS exposure is independently associated with decreased rates of ED visits and hospitalizations. This provides real-world evidence of one potential positive impact of standardized symptom assessment in cancer care.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".