Acute Oncologic Complications: Clinical–Therapeutic Management in Critical Care and Emergency Departments
Bibliographic record
Abstract
Introduction. It is now known that cancer is a major public health problem; on the other hand, it is less known, or rather, often underestimated, that a significant percentage of cancer patients will experience a cancer-related emergency. These conditions, depending on the severity, may require treatment in intensive care or in the emergency departments. In addition, it is not uncommon for a tumor pathology to manifest itself directly, in the first instance, with a related emergency. The emergency unit proves to be a fundamental and central unit in the management of cancer patients. Many cancer cases are diagnosed in the first instance as a result of symptoms that lead the patient’s admittance into the emergency room. Materials and Methods. This narrative review aims to analyze the impact of acute oncological cases in the emergency setting and the role of the emergency physician in their management. A search was conducted over the period January 1981–April 2023 using the main scientific platforms, including PubMed, Scopus, Medline, Embase and Google scholar, and 156 papers were analyzed. Results. To probe into the main oncological emergencies and their management in increasingly overcrowded emergency departments, we analyzed the following acute pathologies: neurological emergencies, metabolic and endocrinological emergencies, vascular emergencies, malignant effusions, neutropenic fever and anemia. Discussion/Conclusions. Our analysis found that a redefinition of the emergency department connected with the treatment of oncology patients is necessary, considering not only the treatment of the oncological disease in the strict sense, but also the comorbidities, the oncological emergencies and the palliative care setting. The need to redesign an emergency department that is able to manage acute oncological cases and end of life appears clear, especially when this turns out to be related to severe effects that cannot be managed at home with integrated home care. In conclusion, a redefinition of the paradigm appears mandatory, such as the integration between the various specialists belonging to oncological medicine and the emergency department. Therefore, our work aims to provide what can be a handbook to detect, diagnose and treat oncological emergencies, hoping for patient management in a multidisciplinary perspective, which could also lead to the regular presence of an oncologist in the emergency room.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".