Bibliographic record
Abstract
HEALTH ISSUE: Although lung cancer is the leading cause of cancer deaths for Canadian women, breast cancer is the most frequently diagnosed. About 5400 women are expected to die from this disease in 2003. In 1998, a woman's lifetime risk of breast cancer was about one in nine. KEY FINDINGS: A number of risk factors for breast cancer have been identified. These include advancing age, hormonal factors (eg. early menarche, late menopause and late age at first full-term pregnancy), familial risk, BRCA-1 and BRCA-2 gene mutations, diet and postmenopausal obesity.Several interventions have been introduced to assist women at high risk for breast cancer, including genetic counseling and testing for women who have strong family histories of breast cancer; selective estrogen receptor modifiers, such as tamoxifen, that has been shown to reduce breast cancer rates; prophylactic mastectomy and screening. DATA GAPS AND RECOMMENDATIONS: Guidelines are unclear in several areas, particularly in screening. Where clinical guidelines are available, health services research or ongoing monitoring (by provincial/territorial cancer agencies) is needed to assess compliance with the guidelines and to ensure equity of access within the provinces/territories.Key components of organized screening programs need to be established, in part to ensure that screening is carried out in high-quality, co-ordinated programs. There is also a need to develop ways to involve women fully in informed decision-making and to address several policy issues to prevent disparities in access to high-quality services. Patenting issues associated with genetic tests also need to be clarified.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".