A narrative review of oncologic emergencies in patients with head and neck cancers: initial management and the role of radiation therapy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Head and neck cancers (HNCs) encompass a complex group of malignancies with high morbidity, often leading to critical emergencies such as pain crises, airway obstruction and hemorrhage. This review aims to outline an evidence-based approach to the multidisciplinary management of HNC oncologic emergencies with a focus on the role of emergent radiotherapy (RT). METHODS: A literature search was performed using Medline, Embase and the Cochrane Central Register of Controlled Trials databases with a focus on three common oncological emergencies using the following keywords: "head and neck cancer", "radiation OR radiotherapy", "pain", "bleeding OR haemorrhage", and "airway obstruction". All English language articles published up to April 2022 were screened to identify studies pertaining to the management of oncologic emergencies in HNC. KEY CONTENT AND FINDINGS: The management of oncologic emergencies in HNC present a unique set of challenges that require early recognition and aggressive treatment. In this narrative review, we summarize the evidence supporting the role of RT in the management of HNC patients presenting with pain crisis, malignant airway obstruction and acute haemorrhage. We demonstrate that while RT can be used as a primary or adjunct therapy, optimal management depends on the involvement of a multi-disciplinary team that includes head and neck surgeons, interventional radiology and palliative care. CONCLUSIONS: RT plays a critical role in the multidisciplinary management of HNC oncological emergencies. Further prospective and comparative studies are needed to assess optimal management strategies.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".