Understanding oncologic emergencies and related emergency department visits and hospitalizations: a systematic review
Bibliographic record
Abstract
BACKGROUND: Patients with cancer frequently visit the emergency department (ED) and are at high risk for hospitalization due to severe illness from cancer progression or treatment side effects. With an aging population and rising cancer incidence rates worldwide, it is crucial to understand how EDs and other acute care venues manage oncologic emergencies. Insights from other nations and health systems may inform resources necessary for optimal ED management and novel care delivery pathways. We described clinical management of oncologic emergencies and their contribution to ED visits and hospitalizations worldwide. METHODS: We performed a systematic review of peer-reviewed original research studies published in the English language between January 1st, 2003, to December 31st, 2022, garnered from PubMed, Web of Science, and EMBASE. We included all studies investigating adult (≥ 18 years) cancer patients with emergency visits. We examined chief complaints or predictors of ED use that explicitly defined oncologic emergencies. RESULTS: The search strategy yielded 49 articles addressing cancer-related emergency visits. Most publications reported single-site studies (n = 34/49), with approximately even distribution across clinical settings- ED (n = 22/49) and acute care hospital/ICU (n = 27/49). The number of patient observations varied widely among the published studies (range: 9 - 87,555 patients), with most studies not specifying the cancer type (n = 33/49), stage (n = 41/49), or treatment type (n = 36/49). Most studies (n = 31/49) examined patients aged ≥ 60 years. Infection was the most common oncologic emergency documented (n = 22/49), followed by pain (n = 20/49), dyspnea (n = 19/49), and gastrointestinal (GI) symptoms (n = 17/49). Interventions within the ED or hospital ranged from pharmacological management with opioids (n = 11/49), antibiotics (n = 9/49), corticosteroids (n = 5/49), and invasive procedures (e.g., palliative stenting; n = 13/49) or surgical interventions (n = 2/49). CONCLUSION: Limited research specifically addresses oncologic emergencies despite the international prevalence of ED presentations among cancer patients. Patients with cancer presenting to the ED appear to have a variety of complaints which could result from their cancers and thus may require tailored diagnostic and intervention pathways to provide optimal acute care. Further acute geriatric oncology research may clarify the optimal management strategies to improve the outcomes for this vulnerable patient population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".