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 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 | 0.133 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".