Do patient-reported symptoms predict for emergency department visits? A population-based analysis.
Bibliographic record
Abstract
6039 Background: Since 2007 in Ontario, Canada, the Edmonton Symptom Assessment System (ESAS) has been routinely used to assess symptoms in cancer patients in both ambulatory and home-care settings. The purpose of this study was to determine the relationship between individual patient symptoms, and their severity, with the likelihood of an emergency department (ED) visit. Methods: The cohort includes all cancer patients in Ontario who completed an ESAS assessment between January 2007 and March 2009. We linked multiple provincial health databases to describe the cohort and determine if an ED visit occurred within 7 days of the patient’s first ESAS. Multivariate logistic regression was used to determine the association between symptom scores (absent: score 0; mild: 1-3; moderate: 4-7; severe: 8-10) and the likelihood of an ED visit. Results: The cohort included 45,118 unique patients whose first assessment contributes to the study. 3.8% (n=1732) had an ED visit. The patients with ED visits were more likely to be men, to have lung or gastro-intestinal cancer, to have had recent radio or chemotherapy, and to have a shorter survival. The proportion of patients with ED visits increased from 2% to 10-12% as individual symptom scores increased from 0 to 10. Anxiety and depression were not associated with ED visits in the model, regardless of severity. Pain, nausea, drowsiness, appetite and shortness of breath with moderate or severe scores were associated with ED visits. Tiredness and wellbeing were the only symptoms to show a significant association for mild, moderate and severe scores. A well being score of 7-10 (reference score=0) had the highest odds ratio of 1.8 (95% CI 1.4-2.3). Conclusions: Worsening symptoms clearly contribute to ED visits. While specific symptoms like pain are obvious targets for management in the outpatient setting, constitutional symptoms like wellbeing or fatigue are associated with even higher odds. Though difficult to manage, such symptoms also warrant detailed assessment in order to optimize patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".