Bibliographic record
Abstract
One in nine women will develop breast cancer in her lifetime (Canadian Cancer Society, 2007). Hereditary breast cancer accounts for only five to 10 per cent of all breast cancers. However, women carrying a single high-penetrance gene mutation have a 40% to 80% chance of developing breast cancer (Fackenthal & Olopade, 2007). Most of these breast cancers occur in women under the age of 50. The BRCA 1 gene mutation was first reported in 1994, and the BRCA 2 gene mutation in 1995. The BRCA 2 gene mutation is often carried in males, and accounts for approximately six per cent of male breast cancer. Women with this gene mutation have a lifetime risk of developing breast cancer of between 50% and 85%, a second breast cancer of between 30% and 50%, and ovarian cancer between 10% and 20%. Each parent with the BRCA 2 gene mutation has a 50% chance of passing this gene mutation to their children (National Cancer Institute, 2006). The emotional impact of receiving cancer risk information such as this is difficult to predict. When presented with information about risk-reduction surgery, chemoprevention, risk avoidance and increased screening, how does one make decisions? Walk with me, as I share how my family discovered we carry the Icelandic founder gene mutation, the steps we took together during the testing process, and the decision-making by the family members who tested positive. We'll focus on my sister Rita--ordinary days, an extraordinary woman.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.469 | 0.182 |
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".