Associations Between Symptom Complexity and Acute Care Utilization Among Adult Advanced Cancer Patients Followed by a Palliative Care Service
Bibliographic record
Abstract
Among adult advanced cancer patients already accessing palliative care, symptoms can contribute to unplanned acute care utilizations, which can disrupt care and worsen patient outcomes. We examined how a novel symptom complexity algorithm, using patients' ratings of the nine Edmonton Symptom Assessment System-Revised (ESAS-r) symptoms to assign "low", "medium", or "high" complexity, predicts acute care utilizations. This retrospective observational cohort study used electronic medical record data from the Durham Regional Cancer Centre in Ontario, Canada, comprising adult advanced cancer patients who completed at least one ESAS-r report between 1 January 2022 and 31 December 2023. We applied chi-squared tests, Kruskal-Wallis H tests, and multivariable binary logistic regressions to evaluate factors associated with higher odds of acute care utilization within seven and fourteen days of patients' first ESAS-r reports after their first palliative care interaction. Of 559 included patients, 125 (22.4%) exhibited low complexity, 180 (32.2%) exhibited medium complexity, and 254 (45.4%) exhibited high complexity on their first ESAS-r report. In total, 61 (10.9%) patients accessed acute care within seven days and 108 (19.3%) patients accessed acute care within fourteen days of their first ESAS-r report. Controlling for sociodemographic and clinical covariates, compared to low-complexity patients, high-complexity patients had higher odds of acute care utilization within seven days (aOR = 2.83, 95% CI: 1.18-6.77), but not within fourteen days (aOR = 1.78, 95% CI: 0.97-3.28). Accordingly, as a clinical decision-making tool, ESAS-r symptom complexity may help identify patients who would benefit from more intensive follow-up and potentially reduce unnecessary acute care utilizations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".