Pan-Canadian Study of Mammography Screening and Mortality from Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Screening with mammography has been shown by randomized controlled trials to reduce breast cancer mortality in women aged 40 to 74 years. Estimates from observational studies following screening implementation in different countries have produced varyied findings. We report findings for seven Canadian breast screening programs. METHODS: Canadian breast screening programs were invited to participate in a study aimed at comparing breast cancer mortality in participants and nonparticipants. Seven of 12 programs, representing 85% of the Canadian population, participated in the study. Data were obtained from the screening programs and corresponding cancer registries on screening mammograms and breast cancer diagnoses and deaths for the period between 1990 and 2009. Standardized mortality ratios were calculated comparing observed mortality in participants to that expected based upon nonparticipant rates. A substudy using data from British Columbia women aged 35 to 44 years was conducted to assess the potential effect of self-selection participation bias. All statistical tests were two-sided. RESULTS: Data were obtained on 2796472 screening participants. The average breast cancer mortality among participants was 40% (95% confidence interval [CI] = 33% to 48%) lower than expected, with a range across provinces of 27% to 59%. Age at entry into screening did not greatly affect the magnitude of the average reduction in mortality, which varied between 35% and 44% overall. The substudy found no evidence that self-selection biased the reported mortality results, although the confidence intervals of this assessment were wide. CONCLUSION: Participation in mammography screening programs in Canada was associated with substantially reduced breast cancer mortality.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".