Oh, Canada: Public Outcry Pushed Demand for Trastuzumab for Early-Stage Breast Cancer
Bibliographic record
Abstract
When positive results from three randomized trials of trastuzumab (Herceptin) in locally invasive breast cancer were released earlier this year, patients with early-stage, HER-2–positive breast cancer in Canada were told they would have to wait: Their doctors couldn't prescribe the drug to them, their hospitals couldn't administer it, and their governments wouldn't pay for it. “Everyone got caught by surprise on this one,” said medical oncologist Brent Schacter , M.D., of Cancer Care Manitoba and chief executive officer of the Canadian Association of Provincial Cancer Agencies. Under the rules governing the use of drugs in Canada, a drug must first be approved by Health Canada, the federal ministry responsible for the health care system. Then, each of Canada's 10 provinces and three territories must agree to put the drug on a “formulary,” which allows doctors to prescribe it, pharmacies to stock it, and patients to get it. When the preliminary trial results—which found that trastuzumab increases progression-free survival, time to first distant recurrence, and overall survival in women with localized invasive breast cancer—were presented at this year's annual meeting of the American Society for Clinical Oncology in May, the drug had been approved in Canada for use only in women with the metastatic form of the disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".