Overdiagnosis by mammographic screening for breast cancer studied in birth cohorts in The Netherlands
Bibliographic record
Abstract
A drawback of early detection of breast cancer through mammographic screening is the diagnosis of breast cancers that would never have become clinically detected. This phenomenon, called overdiagnosis, is ideally quantified from the breast cancer incidence of screened and unscreened cohorts of women with follow-up until death. Such cohorts do not exist, requiring other methods to estimate overdiagnosis. We are the first to quantify overdiagnosis from invasive breast cancer and ductal carcinoma in situ (DCIS) in birth cohorts using an age-period-cohort -model (APC-model) including variables for the initial and subsequent screening rounds and a 5-year period after leaving screening. Data on the female population and breast cancer incidence were obtained from Statistics Netherlands, "Stichting Medische registratie" and the Dutch Cancer Registry for women aged 0-99 years. Data on screening participation was obtained from the five regional screening organizations. Overdiagnosis was calculated from the excess breast cancer incidence in the screened group divided by the breast cancer incidence in presence of screening for women aged 20-99 years (population perspective) and for women in the screened-age range (individual perspective). Overdiagnosis of invasive breast cancer was 11% from the population perspective and 17% from the invited women perspective in birth cohorts screened from age 49 to 74. For invasive breast cancer and DCIS together, overdiagnosis was 14% from population perspective and 22% from invited women perspective. A major strength of an APC-model including the different phases of screening is that it allows to estimate overdiagnosis in birth cohorts, thereby preventing overestimation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".