Evolving Best Practice for Take-Home Cancer Drugs
Bibliographic record
Abstract
PURPOSE: Take-home cancer drugs (THCDs) have become a standard treatment of many cancers. Robust guidelines have been developed for intravenous chemotherapy drugs, but few exist for THCDs with a focus on decentralized models. Hence, Ontario Health (Cancer Care Ontario) established the Oncology Pharmacy Task Force (OPTF) to develop consensus-based recommendations on best practices for THCDs to ensure that patients receive safe, consistent, high-quality care in the community once they leave the cancer center/practice with a prescription. METHODS: The OPTF included 34 members with comprehensive representation. Guidance from leading authorities was extracted through literature review, thematically analyzed, and synthesized to develop 29 recommendations. The consensus process (> 70% agreement) included a three-step modified Delphi method followed by an extensive review process. RESULTS: Sixteen recommendations were developed: training and education for providers (2), drug access (1), prescribing (4), patient and family/caregiver education (3), communication (1), dispensing (3), monitoring for patient adherence and adverse effects (1), and incident reporting (1). CONCLUSION: Through a rigorous methodology, the OPTF derived a robust set of recommendations similar to the ASCO/Oncology Nursing Society and ASCO/National Community Oncology Dispensing Association guidelines, further validating and strengthening the applicability across multiple jurisdictions, including those with decentralized models. Unique aspects in a decentralized model include the need for two pharmacy professionals, with one doing cognitive verification of the script and the other dispensing the medication; moreover, they optimize interprofessional communication between community providers and the cancer center/practice health care team.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".