Cross-Border Referral for Early Breast Cancer: An Analysis of Radiation Fractionation Patterns
Bibliographic record
Abstract
Because of increasing waiting times for adjuvant radiation in the province of Ontario, patients from one Canadian centre were referred to two centres in the United States. This situation provided an opportunity to compare radiation practices. We performed a retrospective review of radiation prescribed to patients following breast-conserving surgery for invasive breast cancer. Patients with positive margins, 4 or more positive lymph nodes, recurrent disease, or large tumours (>5 cm) were excluded. For comparison, we reviewed a random sample of similar patients treated at the Canadian centre during the same period. A total of 120 referred and 217 non-referred patients were eligible for comparison. The analysis included 98 pairs of patients (N = 196), fully matched on age, nodal status, T stage, grade, and estrogen receptor (ER) status. Mean patient age was 60.7 years. The median total dose and number of fractions differed between centres [6040 cGy in 32 fractions (United States) vs. 4250 cGy in 16 fractions (Canadian), both p < 0.001). Boost was used more often in the United States (97% vs. 9%, p < 0.001). Variation in prescribing patterns was seen. In the United States, seven different schedules for whole-breast irradiation were used; at the Canadian centre, two schedules were prescribed. Predicted radiobiologic effects of these schedules were calculated to be similar. Differences in fractionation patterns were observed between and within U.S. and Canadian centres. Such variability is likely to affect patient convenience and resource utilization. Although patient selection, referring surgeon, and change in policies may account for some of the observed differences, further research is necessary to better understand the causes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".