Abstract A44: The prevalence of prognostic and treatment features for breast cancer survival: Are they different for First Nations women compared to other women in Ontario, Canada?
Bibliographic record
Abstract
Background: There is growing literature in the United States proposing that the distribution of some prognostic features for cancer may vary by ethnic/racial status. Among Canadian cancer registries, data on prognostic features, as well as treatment, ethnicity, socioeconomic status, are not routinely collected. To determine these characteristics, data from medical charts of the cancer patient is abstracted. Previous work in Ontario, Canada has demonstrated that survival after a breast cancer diagnosis is poorer among First Nations women compared to other Ontario women. The purpose of this study is to identify the distribution of prognostic features and treatment received among First Nations and non-First Nations women diagnosed with breast cancer from 1995 to 2004 in Ontario. Study Design: This study employed a case-case design using the cohort of First Nations people in Ontario to identify 297 women diagnosed with invasive breast cancer between 1995 and 2004. Concurrently, a random sample of 694 non-First Nations women were selected through the population-based cancer registry at Cancer Care Ontario and matched 2 to 1 on five-year date of diagnosis, age at diagnosis (15–54 vs. 55+), and Integrated Cancer Program (ICP) first attended. Data on stage at diagnosis, treatment received, risk factors and co-morbid conditions was collected from medical charts at the provincial ICP. Analysis: Univariate analyses were calculated by First Nations status. Results: A similar proportion of First Nations women are screened with mammography; stage at diagnosis is later for First Nations women (71% are diagnosed at stage II or higher compared to 55% among other Ontario women); proportions of women receiving chemo/hormonal therapy are similar; proportion of women undergoing biopsies and surgeries are similar, however the distribution of each procedure is different (for instance, modified radical mastectomies are 14% higher among First Nations women); and proportion of women having radiotherapy are lower for First Nations women. Conclusions: It is important to identify the prognosis and treatment factors that influence breast cancer survival in First Nations women. These preliminary findings will be further explored and controlled for variables such as socioeconomic status and other tumor characteristics. Once better understood, actions can be taken to improve the prognosis and treatment of First Nations women with breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".