Breast cancer mortality after screening mammography in British Columbia women
Bibliographic record
Abstract
Mammographic screening is a proven method for the early detection of breast cancer. The authors analyzed the impact of service mammographic screening on breast cancer mortality among British Columbia women who volunteered to be screened by the Screening Mammography Program of British Columbia. A cohort of women having at least one mammographic screen by Screening Mammography Program of British Columbia between the ages of 40 and 79 in the period 1988-2003 was identified. All cases and deaths from breast cancer occurring in British Columbia were identified from the British Columbia Cancer Registry and linked to the screening cohort. Expected deaths from breast cancer in the cohort were calculated using incidence and survival rates for British Columbia women not in the cohort. Adjustment was made for age and socioeconomic status of their area of residence at time of diagnosis. The breast cancer mortality ratio was calculated by dividing observed by expected breast cancer deaths. The mortality ratio (95% confidence interval) was 0.60 (0.55, 0.65) for all ages combined (p < 0.0001). The mortality ratio in women aged 40-49 at first screening was 0.61 (0.52, 0.71), similar to that in women over 50 (p = 0.90). Exclusion of mortality associated with breast cancers diagnosed after age 50 in women starting screening in their 40s increased the mortality ratio to 0.63 (0.52, 0.77), but it remained statistically significant. Correction for self-selection bias using estimates from the literature increased the mortality ratio for all ages to 0.76. Mammographic screening at all ages between 40 and 79 reduced subsequent mortality rates from breast cancer.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".