Oral chemotherapy practices at Ontario cancer centres
Bibliographic record
Abstract
PURPOSE: The use of oral chemotherapy agents in cancer treatment is increasing. To better understand issues affecting the optimal use of these agents, Cancer Care Ontario conducted an environmental scan of current practices in Ontario related to prescribing, dispensing, patient education, and supporting regimen adherence. METHODS: A series of semi-structured interviews were conducted either by phone (11 regions) or via email (two regions) with Ontario's Regional Cancer Centres over a 3-month period in 2012. A questionnaire was pre-circulated to the regions to guide the discussions. RESULTS: Responses were received from 13 of 14 regions. Considerable variation in practice was found. Of 13 responding regions, 12 (92%) lacked formal procedures or processes for the prescription of oral chemotherapy. Ten regions (77%) reported using either handwritten prescriptions or a mixture of methods with only three regions routinely using computerized order entry systems for oral chemotherapy prescribing. Oral chemotherapy was reported to be labeled as "chemotherapy" in 46% of the regions. Twenty-three percent indicated that they provide extensive patient education through a multi-disciplinary approach. A number of tools were used to encourage patient adherence in different regions. Patient education was identified as an area where more work could be done. CONCLUSION: Results indicate a lack of formal policies and variable practices across all aspects of oral chemotherapy in many regions. However, some regions have developed and implemented successful initiatives. The results from this review are informing provincial priorities and being shared between regions to support collaborative learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".