The impact of routine Edmonton Symptom Assessment System (ESAS) use on overall survival in cancer patients: Results of a population‐based retrospective matched cohort analysis
Bibliographic record
Abstract
BACKGROUND: 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 cancer patients. The objective was to examine the effect of ESAS exposure on overall survival. We hypothesized, a priori, that patients exposed to ESAS would have higher rates of overall survival than those who were not exposed. METHODS: This was a retrospective matched cohort study of adults diagnosed with cancer between 2007 and 2015. Patients were considered exposed if they were screened with ESAS at least once during the study period. Their first ESAS screening date defined the index date. Each exposed patient was matched randomly to a cancer patient without ESAS using a combination of hard matching (4 variables) and propensity score matching (14 variables). Kaplan-Meier curves and multivariable Cox regression were used to evaluate the impact of ESAS exposure on survival. RESULTS: There were 128,893 pairs well matched on all baseline characteristics. The probability of survival within the first 5 years was higher among those exposed to ESAS compared to those who were not (81.9% vs. 76.4% at 1 year, 68.3% vs. 66.1% at 3 years, 61.9% vs. 61.4% at 5 years, P-value < .0001). In the multivariable Cox regression model, ESAS was significantly associated with a decreased mortality risk (HR: 0.48, 95% CI: 0.47-0.49). CONCLUSIONS: Our results show that ESAS exposure is associated with improved survival in cancer patients. This provides real world evidence of the impact of routine 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".