Outcomes after emergency department use in patients with cancer receiving chemotherapy in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Patients with cancer frequently require emergency medical care during treatment. The objective of this study was to characterize emergency department visits made by patients with cancer receiving chemotherapy and to describe associated outcomes. METHODS: This retrospective cohort study used population-based administrative data from Ontario, Canada. Patients aged 18 years and older, with a cancer diagnosis, and who received chemotherapy in the 30 days before being seen in an emergency department between 2013 and 2017 were included. Emergency department discharge diagnosis codes were categorized to identify the most frequent emergency department diagnoses. We examined the proportion of patients admitted to hospital and 30-day mortality. We used logistic regression to identify predictors of hospital admission. RESULTS: We identified 218 459 emergency department visits made by 87 555 patients. The median number of emergency department visits per patient was 2 (interquartile range 1-3). Hematological, gastrointestinal, breast and lung cancer were the most common malignancies represented. The most common emergency department diagnoses were infection or fever (57 036 [26.1%]) and gastrointestinal diagnoses (26 456 [12.2%]). Of all visits, 77 978 (35.7%) resulted in admission to hospital. Thirty-day mortality after an emergency department visit was 9.8%. There was an increased odds of admission among patients who previously received palliative consultation, patients with bone or soft tissue or hematological malignancies, and patients with infection, gastrointestinal, pulmonary, cardiac, weakness or genitourinary and nephrology diagnoses. INTERPRETATION: Patients with cancer frequently used the emergency department during chemotherapy, and 1 in 4 emergency department visits were for infection or fever. These results highlight opportunities to optimize care for certain patients being actively treated for cancer, particularly around infectious complaints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".