Incidence of breast cancer and estimates of overdiagnosis after the initiation of a population-based mammography screening program
Bibliographic record
Abstract
BACKGROUND: There has been growing interest in the overdiagnosis of breast cancer as a result of mammography screening. We report incidence rates in British Columbia before and after the initiation of population screening and provide estimates of overdiagnosis. METHODS: We obtained the numbers of breast cancer diagnoses from the BC Cancer Registry and screening histories from the Screening Mammography Program of BC for women aged 30-89 years between 1970 and 2009. We calculated age-specific rates of invasive breast cancer and ductal carcinoma in situ. We compared these rates by age, calendar period and screening participation. We obtained 2 estimates of overdiagnosis from cumulative cancer rates among women between the ages of 40 and 89 years: the first estimate compared participants with nonparticipants; the second estimate compared observed and predicted population rates. RESULTS: We calculated participation-based estimates of overdiagnosis to be 5.4% for invasive disease alone and 17.3% when ductal carcinoma in situ was included. The corresponding population-based estimates were -0.7% and 6.7%. Participants had higher rates of invasive cancer and ductal carcinoma in situ than nonparticipants but lower rates after screening stopped. Population incidence rates for invasive cancer increased after 1980; by 2009, they had returned to levels similar to those of the 1970s among women under 60 years of age but remained elevated among women 60-79 years old. Rates of ductal carcinoma in situ increased in all age groups. INTERPRETATION: The extent of overdiagnosis of invasive cancer in our study population was modest and primarily occurred among women over the age of 60 years. However, overdiagnosis of ductal carcinoma in situ was elevated for all age groups. The estimation of overdiagnosis from observational data is complex and subject to many influences. The use of mammography screening in older women has an increased risk of overdiagnosis, which should be considered in screening decisions.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".