Therapeutics for paediatric oncological emergencies
Bibliographic record
Abstract
BACKGROUND: With advancements in the field of oncology, cancer survival rates have improved dramatically but modern cancer treatments also come with an increasing number of disease and treatment-associated complications. This article provides an updated narrative review on the pathophysiology, clinical presentations and latest management strategies for common paediatric oncological emergencies. METHODS: search of all human studies in the English literature was performed in Clinical Queries for different oncology syndromes and conditions using the following Medical Subject Headings: "tumour lysis syndrome", "hyperleukocytosis", "disseminated intravascular coagulation", "superior mediastinal syndrome", "superior vena cava syndrome", "sepsis", "severe inflammatory response syndrome", "acute respiratory distress syndrome", "posterior reversible encephalopathy syndrome" and "reversible posterior leukoencephalopathy syndrome". Categories were limited to clinical trials and reviews for ages from birth to 18 years. RESULTS: The general description, presentation and management of these oncologic emergencies are systematically described. Early recognition along with prompt and proactive treatment can reduce the chances of potential complications and improve the clinical outcomes, thereby improving not only survival rates in oncology patients but also their clinical outcomes and quality of life. CONCLUSIONS: Oncologic emergencies are associated with significant mortality and morbidity. Healthcare professionals involved with the care of oncology patients must be vigilant of these emergencies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".