The effect of population‐based mammography screening in Dutch municipalities on breast cancer mortality: 20 years of follow‐up
Bibliographic record
Abstract
Long-term follow-up data on the effects of screening are scarce, and debate exists on the relative contribution of screening versus treatment to breast cancer mortality reduction. Our aim was therefore to assess the long-term effect of screening by age and time of implementation. We obtained data on 69,630 breast cancer deaths between 1980 and 2010 by municipality (N = 431) and age of death (40-79) in the Netherlands. Breast cancer mortality trends were analyzed by defining the municipality-specific calendar year of introduction of screening as Year 0. Additionally, log-linear Poisson regression was used to estimate the turning point in the trend after Year 0, per municipality, and the annual percentage change (APC) before and after this point. Twenty years after introduction of screening breast cancer mortality was reduced by 30% in women aged 55-74 and by 34% in women aged 75-79, compared to Year 0. A similar and significant decrease was present in municipalities that started early (1987-1992) and late (1995-1997) with screening, despite the difference in availability of effective adjuvant treatment. In the age groups 55-74 and 75-79, the turning point in the trend in breast cancer mortality was estimated in Years 2 and 6 after the introduction of screening, respectively, after which mortality decreased significantly by 1.9% and 2.6% annually. These findings show that the implementation of mammography screening in Dutch municipalities is associated with a significant decline in breast cancer mortality in women aged 55-79, irrespective of time of implementation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".