The impact of routine ESAS use on receiving palliative care services: Results of a population-based retrospective matched cohort analysis.
Bibliographic record
Abstract
191 Background: In 2007 Cancer Care Ontario began standardized symptom assessment as part of routine clinical care using the Edmonton Symptom Assessment System (ESAS). The purpose of this project was to evaluate the impact of this program on referrals to palliative care. We hypothesized that patients exposed to ESAS would be more likely to be referred. Methods: A retrospective matched cohort study was conducted to examine the impact of ESAS screening on the initiation of palliative care services provided by physician or homecare nurse among newly diagnosed cancer patients in Ontario, Canada. The study included all adult patients who were diagnosed with cancer between 2007 and 2015. Exposure was defined as completing ≥1 ESAS during the study period. Using four hard matched variables and propensity-score matching with 14 variables, cancer patients exposed to ESAS were matched 1:1 to those who were not. Matched patients were followed from first ESAS until initiation of palliative care, death or the end of study at Mar 31, 2017. Results: The final cohort consisted of 204,688 matched patients with no prior palliative care consult. The pairs were well matched. The probability of receiving palliative care within the first 5 years was higher among those exposed to ESAS compared to those who were not (20.6% vs. 15.2%, p < .0001). The risk of death without receipt of palliative care within the same period was low in both groups. In the adjusted cause-specific Cox proportional hazards model, ESAS assessment was associated with a 6% increase in palliative care services (HR: 1.06, 95% CI: 1.04-1.08). Conclusions: Cancer patients who completed ESAS were more likely to initiate palliative care services than those who didn’t. ESAS screening may help identify patients who would benefit from a palliative approach to care earlier in their clinical course.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".