Human Small Breast Epithelial Mucin: The Promise of a New Breast Tumor Biomarker
Bibliographic record
Abstract
Breast cancer remains one of the most frequently diagnosed cancers today. In developed countries, one in eight women is expected to present with breast cancer within her lifetime and an estimated 1,000,000 cases are detected each year worldwide (Canadian Cancer Statistics, http://www.cancer.ca/vgn/images/ portal/cit_86751114/14/33/1959864 11niw_stats2004_en.pdf). For women with recurrent disease, the median time of survival is about 2 years. Despite optimal surgery, adjuvant irradiation, hormonal treatment, and chemotherapy, approximately 30% of patients with localized breast cancer finally develop distant metastases. Early detection, which enables intervention at a localized and potentially curable stage, remains a central goal in breast cancer treatment. Indeed, the 5-year survival rate for women with breast cancer has been shown to increase dramatically when the disease is diagnosed at an early stage: from less than 25% in women with disseminated cancer to about 75% in patients with regional disease and over 95% in women with a localized tumor (Breast Cancer Facts and Figures, 2001-2002, http://www.cancer.org/downloads/STT/BrCaFF 2001.pdf). Unfortunately, only 60% of all breast cancers are diagnosed at a local stage. Any improvement in early detection through identification of tumor biomarkers would have a significant impact on reducing overall breast cancer mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".