Comparison of clinical characteristics and survival of metastatic breast cancer patients in United States according to insurance status as compared to outcomes in Canada.
Bibliographic record
Abstract
e18617 Background: Insurance status affects cancer survival in the US. In the Canadian system, health care access is similar for the whole population. The purpose of this study was to compare clinical characteristics and survival of metastatic breast cancer (MBC) patients in a Canadian province and in the US according to insurance status. Methods: US SEER and the Breast Data Mart, Alberta, Canada data were queried to obtain 20 to 64yo pts diagnosed with MBC from 2007-2012. Pts ≥65yo were excluded due to unreliable insurance status classification in the US. OS curves were calculated using the Kaplan-Meier method and the 50th percentile was estimated. Differences in OS were assessed with the log-rank test. Multivariate analysis was performed using the Cox proportional hazards model. Results: 10,927 pts from SEER and 392 pts from Canada were included. Most common age group among US and Canada pts was 60-64y (23.9%; 24.8%). Most common breast subtype in the US and Canada was HR+HER2- (58%; 61%), followed by any HER2+ (28%; 31%) and TN (15%; 8%). Median follow-up was 32m. Median OS in Canada was shorter than in insured (27.4 vs 36m), similar to Medicaid (27.4 vs 24m) and all groups had longer OS than uninsured (18m, P < 0.001). An adjusted Cox regression yielded the same results, with a worse OS in Canada relative to insured [HR = 0.71 95%CI 0.63-0.8 P < 0.001], similar OS to Medicaid [HR = 1.02 95%CI 0.90-1.15 P = 0.78] and better OS than uninsured [HR = 1.23 95%CI 1.12-1.5 P = 0.0005]. In subgroup analysis based on breast subtype, shortest Canadian median OS was for TN (15.2m), followed by HR+HER2- (30.9m), HR+HER2+ (44.3m) and HR-HER2+ (51.4m, P = 0.37). In the US, shortest median OS was from TN (13m), followed by HR-HER2+ (33m), HR+HER2- (39m) and HR+HER2+ (45m, P < 0.0001). Conclusions: Among MBC patients in the selected population, the Canadian group resembled the US Medicaid group for survival outcomes. In the US population, Medicaid and uninsured patients experienced worse survival when compared to insured.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".