Reducing emergency department utilization for outpatient acute cancer symptoms: An integrative review on the advent of urgent cancer clinics
Bibliographic record
Abstract
The purpose of this integrative literature review was to identify nursing research opportunities related to outpatient acute cancer symptom management within emerging urgent cancer clinics (UCCs). Patients with acute cancer symptoms (e.g., fevers, gastrointestinal disturbances, or uncontrolled pain) from ambulatory settings predominantly rely on emergency departments (EDs) for assessment and treatment. However, this model of care is no longer sustainable and emphasizes healthcare system inefficiencies. Urgent cancer clinics allow patients to have these symptoms treated by oncology experts within ambulatory cancer centres. Unfortunately, limited research on urgent cancer clinics both operationally and experientially makes it difficult for others to adopt this new model of care. The core questions that guided this integrative review were: 1) What is the state of the science regarding UCCs, and what differences exist when compared to EDs in the management of outpatient acute cancer symptoms? and 2) Where do UCCs exist around the world, and what is understood about UCCs related to clinic operations and staffing models?
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".