Building a Dream: Creating an Oncology Day/Evening Hospital
Bibliographic record
Abstract
The demand for inpatient beds has reached and often exceeds capacity producing waiting lists for cancer care. There is a need to explore alternative approaches to oncology treatment. The Oncology Day/Evening Hospital (ODEH), originally envisioned in 1995 as a joint project between an ambulatory cancer centre and a large teaching hospital, is an important cancer treatment initiative offering extended hours of ambulatory oncology treatment on days, evenings, weekends and statutory holidays. A review of current inpatient treatment modalities revealed that many patients receiving inpatient therapy could be safely and effectively managed in the ambulatory setting if treatment regimens were modified and if ambulatory hours of operation were extended. Healthcare improvements expected were: appropriate movement of inpatient activity to the ambulatory setting; more opportunities for patient choice in treatment time thereby allowing for maintenance of normal living; better quality of life for patients through prevention of hospitalization; decrease in treatment waiting times; consolidation of patients into an ambulatory oncology treatment setting as opposed to utilization of adult medicine units; and more rational inpatient bed utilization with reduction of admissions and intra-treatment transfers. This article describes our experience in building a dream, the challenges and lessons learned in implementing a better way to deliver oncology care in an environment of rapid change and staff shortages.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".