Less Than Ideal: How Oncologists Practice With Limited Drug Access
Bibliographic record
Abstract
PURPOSE: To evaluate Canadian medical oncologists' perspectives on how barriers to accessing new expensive cancer drugs have affected their practice and their opinions on the drug approval and funding processes. METHODS: Canadian medical oncologists treating colorectal cancer (CRC) were surveyed by means of a self-administered, cross-sectional survey. RESULTS: Of the 164 eligible oncologists, there were 68 respondents (41.4% response rate). Only 29.4% of physicians felt they had been using the ideal first-line chemotherapy regimen for patients with metastatic CRC. Although all considered bevacizumab to be a component of the ideal first-line regimen, only 18% could use bevacizumab routinely, and less than half (44.8%) always discussed its role with their patients. In terms of accessing unfunded drugs, most physicians agreed that private payment should be allowed for drugs to be delivered at their own centers (76.1%) or private infusion clinics (52.2%). Ninety-seven percent of physicians reported major concerns about the drug approval and funding processes, and 85% of physicians supported the establishment of a national drug formulary. CONCLUSIONS: Canadian medical oncologists are struggling to provide optimal cancer care for their patients with metastatic CRC as a result of nonuniform access to preferred therapeutic drugs. In face of these challenges, physicians have had to use clinical trials and private infusion clinics and, at times, may avoid discussing drugs with limited access. Many oncologists are dissatisfied with the existing funding mechanism and approval processes and support private payment for unfunded drugs.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".