A Framework for the Organization and Delivery of Systemic Treatment
Bibliographic record
Abstract
BACKGROUND: Increasing systemic treatment and shortages of oncology professionals in Canada require innovative approaches to the safe and effective delivery of intravenous (IV) cancer treatment. We conducted a systematic review of the clinical and scientific literature, and an environmental scan of models in Canada, the United Kingdom, Australia, and New Zealand. We then developed a framework for the organization and delivery of IV systemic treatment. METHODS: The systematic review covered the medline, embase, cinahl, and HealthStar databases. The environmental scan retrieved published and unpublished sources, coupled with a free key word search using the Google search engine. The Systemic Treatment Working Group reviewed the evidence and developed a draft framework using evidence-based analysis, existing recommendations from various jurisdictions, and expert opinion based on experience and consensus. The draft was assessed by Ontario stakeholders and reviewed and approved by Cancer Care Ontario. RESULTS: The poor quantity and quality of the evidence necessitated a consensus-derived model. That model comprises four levels of care determined by a regional systemic treatment program and three integrated structures (integrated cancer programs, affiliate institutions, and satellite institutions), each with a defined scope of practice and a specific organizational framework. INTERPRETATION: New models of care are urgently required beyond large centres, particularly in geographically remote or rural areas. Despite limited applicable evidence, the development and successful implementation of this framework is intended to create sustainable, accessible, quality care and to measurably improve patient outcomes.
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 | 0.059 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.011 | 0.005 |
| 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".