Responding to Acute Care Needs of Patients With Cancer: Recent Trends Across Continents
Bibliographic record
Abstract
Remarkable progress has been made over the past decade in cancer medicine. Personalized medicine, driven by biomarker predictive factors, novel biotherapy, novel imaging, and molecular targeted therapeutics, has improved outcomes. Cancer is becoming a chronic disease rather than a fatal disease for many patients. However, despite this progress, there is much work to do if patients are to receive continuous high-quality care in the appropriate place, at the appropriate time, and with the right specialized expert oversight. Unfortunately, the rapid expansion of therapeutic options has also generated an ever-increasing burden of emergency care and encroaches into end-of-life palliative care. Emergency presentation is a common consequence of cancer and of cancer treatment complications. It represents an important proportion of new presentations of previously undiagnosed malignancy. In the U.K. alone, 20%-25% of new cancer diagnoses are made following an initial presentation to the hospital emergency department, with a greater proportion in patients older than 70 years. This late presentation accounts for poor survival outcomes and is often associated with poor patient experience and poorly coordinated care. The recent development of acute oncology services in the U.K. aims to improve patient safety, quality of care, and the coordination of care for all patients with cancer who require emergency access to care, irrespective of the place of care and admission route. Furthermore, prompt management coordinated by expert teams and access to protocol-driven pathways have the potential to improve patient experience and drive efficiency when services are fully established. The challenge to leaders of acute oncology services is to develop bespoke models of care, appropriate to local services, but with an opportunity for acute oncology teams to engage cancer care strategies and influence cancer care and delivery in the future. This will aid the integration of highly specialized cancer treatment with high-quality care close to home and help avoid hospital admission.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".