Wait times for surgical and adjuvant radiation treatment of breast cancer in Canada and the United States: Greater socioeconomic inequity in America
Bibliographic record
Abstract
PURPOSE: The demand for cancer care has increased among aging North American populations as cancer treatment innovations have proliferated. Gaps between supply and demand may be growing. This study examined whether socioeconomic status has a differential effect on waits for surgical and adjuvant radiation treatment (RT) of breast cancer in Canada and the US. METHODS: Ontario and California cancer registries provided 929 and 984 breast cancer cases diagnosed between 1998 and 2000 in diverse urban and rural places. Residence-based socioeconomic data were taken from censuses. Cancer care variables were reliably abstracted from health records: stage, receipt of surgery and RT, and waits from diagnosis to initial and initial to adjuvant treatment. Median waits were compared within- and between-country with the non-parametric Mann-Whitney U-test. Categorically long, age-adjusted wait comparisons used the Mantel-Haenszel chi-square test. RESULTS: There were significant associations between lower socioeconomic status and longer surgical waits, lower access to adjuvant RT and to longer RT waits across diverse places in California. None were observed in Ontario. The two cohorts did not practically differ on access to surgery or on surgical waits. Compared with their counterparts in California, low-income Ontarians, particularly those in small urban places, gained greater access to RT, while high-income Americans had shorter waits for RT. CONCLUSIONS: This historical study contextualized Canada's "waiting-list problems" with evidence on breast cancer care, where lower income Americans seemed to have waited as long as similar Canadians. Many more low-income Americans seemed to experience the longest wait of all for adjuvant care. They simply did not receive it. In contrast to stark American socioeconomic inequity, this study evidenced remarkable equity in Canadian breast cancer care.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".