Estimation of additional MRI resources needed in British Columbia for screening high-risk women for breast cancer.
Bibliographic record
Abstract
51 Background: Screening women at high risk with MRI has been shown to detect breast cancer at an early stage. Therefore, MRI screening has been recommended in the UK and USA for women who are at a high risk of developing breast cancer. However, there is no information available in the province of British Columbia (BC) about the number of women who have a high risk of developing breast cancer. Therefore, we carried out a study to estimate the breast cancer risk distribution in three sample populations in BC using Tyrer-Cuzick (TC) risk prediction model so that additional resource requirement for MRI breast screening can be calculated. Methods: A survey questionnaire was designed based on the TC model, which includes family history, hormonal factors, and benign breast disease. Additional questions also include factors that are used in other models (Gail, Claus, and BCRAPRO) as well as factors that may be included in the future. Women were recruited by staff and volunteers at three screening mammography clinics: Kelowna, Victoria General Hospital, and BC Women’s Health Centre in Vancouver. The survey was available to women to complete on the web, by phone, or on paper. An online database was constructed to store and query the data. The 10-year risk of developing breast cancer for each woman was calculated using the Tyrer-Cuzick IBIS Risk Evaluator software and the risk distribution of the survey population was analyzed. Results: Data from 3,200 women recruited from three sites, gives a risk distribution showing 2.6% are at high risk of developing breast cancer, 31.2% are at moderate risk, and 66.2% are at low risk. Based on NICE guidelines (UK), high risk is defined as having a 10-year risk of greater than 8%, moderate risk as 3-8%, and low risk as less than 3%. Extrapolating this to the approximately 500,000 women who are eligible to attend for screening mammography in BC, 13,000 women are considered at high risk. Conclusions: Our results indicate that 2.6% of women ages 40-79 attending screening mammography in BC may have a very high risk of developing breast cancer based on personal and family history. Based on a 14-hour work day, three additional MRI scanners would be required to implement MRI screening for these high-risk women in BC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".