Health care practitioner prescribing patterns and perspectives on oral chemotherapy management: A survey of cancer centers in Toronto, Canada.
Bibliographic record
Abstract
48 Background: With an increasing number of patients receiving oral cancer therapies, evaluation of safe prescription practices, effective patient education, and toxicity monitoring of these agents is imperative. Methods: Multi-disciplinary oncology practitioners at several cancer centres in Toronto, Canada were surveyed using a web-based platform, to evaluate their prescription practices, use of patient education and symptom management tools, as well as their views on patient adherence and toxicity reporting. Results: Of 170 respondents, 43% were nurses, 34% were pharmacists, and 23% were physicians. Seventy nine percent considered patient education, medication adherence (76%), and toxicity management (78%) as “very important” components of oral chemotherapy management. Prescription methods varied: 59% of respondents used written prescriptions, 39% computerized physician order entry (CPOE), and 0% pre-printed orders, ≥50% of the time. Clinicians felt that patients report side effects from oral agents only “some of the time” (53%), and the most problematic toxicities were nausea (61%) and diarrhea (61%). Practitioners perceived the most common reasons for patient underreporting of side effects to be “fear of treatment interruption” (62%), and that “toxicities are part of the treatment” (66%). Seventy three percent of those surveyed felt individual counseling, follow-up calls (69%), and updated medication information (57%) would improve patient adherence and safety. Conclusions: A diverse group of surveyed oncology professionals expressed the importance of utilizing educational and toxicity monitoring tools for patients on oral cancer therapies, particularly as patients are thought to under-report symptoms. Prescription practices are variable, and CPOE use should be improved. The results of this survey will also be compared to a patient survey, to help develop better tools and policies to standardize practice, and improve patient adherence and toxicity management on oral cancer agents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".