Increased incidence of invasive breast cancer after the introduction of service screening with mammography in Sweden
Bibliographic record
Abstract
Jonsson et al. find considerable overdiagnosis after introduction of service screening with mammography in Sweden.1 Even after adjustment for lead time, they find relative risks of 1.54 for the age group 50–59 years and 1.21 for 60–69 years. The authors analyzed the stabilized phase which they defined as the incidence from year 7 and forward after introduction of screening, and compared with the period before screening. I agree with their approach but wonder why the authors write that the randomized screening trials cannot be used for estimation of the level of overdiagnosis. They argue that the time before the control group was invited to screening was too short in the trials in Sweden and elsewhere. However, according to their own criteria, this is not correct. There are data from the trials in Canada and Malmö after 7 and 8.8 years of follow-up where the control group had not been invited to screening.2, 3 We have reported a relative risk of 1.30 (95% CI 1.20–1.40) for number of cancers4 and a similar increase in number of mastectomies and tumorectomies.5 We have also analyzed the other Swedish trials, looking only at the period before the control group was invited to screening, and found similar results, relative risk 1.33 (1.24–1.44).4 We are aware that even after 7–9 years, some minor effect of lead time would remain in the trials, but opportunistic screening in the control group is a bias that goes in the opposite direction. We therefore think that our findings are reasonably reliable and that Jonsson et al. have confirmed them with epidemiological data. Yours sincerely, Peter C. Gøtzsche.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".