Changing demographics of locally advanced breast cancer: Data from a regional cancer centre
Bibliographic record
Abstract
10780 Background: Locally advanced breast cancer (LABC) (including inflammatory breast cancer (IBC)) accounts for less than 5% of women diagnosed with breast cancer in North America each year. This population of women continues to represent a challenge in terms of timely diagnosis and treatment. Methods: A retrospective database was developed using the American Joint Committee on Cancer (AJCC)2002 staging classification for all women who presented to TOHRCC with LABC between Jan 1/02 - April 1/05. Information was abstracted from clinic charts and the patient self-reported health questionnaires. Results: These results reflect the demographics of the first 50 women entered into our database. Median age at presentation was 57 years (range 28–88); 62% were post-menopausal and 28% had a 1st/2nd degree relative with breast cancer. Clinical diagnosis was made by: self-detection (79%); mammography (5%), routine physical exam (9%) and CT scan (2%). Clinical tumour stage at presentation was: IIIA (25.6%); IIIB (53.5%) and IIIC (9.3%). The majority of women were diagnosed with infiltrating ductal carcinoma (72%). Women with T4d tumours (IBC) (38%) tended to be younger (54.5 vs 59.2 years); presented earlier (2.7 vs. 6.3 months); had larger tumours at the time of diagnosis (9.7 vs 5.5 cm); were more likely grade III (30 vs 20%) and were more often ER negative (42.1% vs 33.3%) and PR negative (63.2% vs. 50%). Only 13% of women in this database were tested for HER-2 of whom 70% were positive. Conclusions: This data utilizing the new AJCC (2002) staging system reflects important shifts in LABC that will influence clinical care in the future. Compared to historical databases, patients tended to be younger and have more aggressive disease including ER negative and HER-2 positive disease. Supplemental microarray studies to further explore this entity are planned. We will present clinical management outcomes in an additional submission. No significant financial relationships to disclose.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".