Comments to the letters by Per‐Henrik Zahl and Jan Maehlen and by Peter C. Gotzsche concerning our article: Increased incidence of invasive breast cancer after the introduction of service screening with mammography in Sweden
Bibliographic record
Abstract
Dr. Gotzsche questions our statement that the randomized trials are less well suited for estimation of the level of overdiagnosis. In fact, we wrote that “in general” the trial time, i.e., the time the study group was invited to screening and the control group not invited, was too short in the Swedish trials and elsewhere to study the incidence when it has stabilized after the start of screening. We agree that the Malmö trial had a trial time long enough to study the stabilized incidence. However, in the Canadian trial, only 5 annual mammography examinations were accomplished for the invited group.1 Gotzsche gives a relative risk figure for incidence in the trials at Canada and Malmö.2 However, there are some reasons why this figure is not comparable with ours. The relative risk incidence figures were based on the first 7 and 8.8 years of follow-up. In our paper, we analyzed the incidence 7–15 years after the screening started. The first round was included in their analysis. The objective of mammography screening is to detect cancer early, which will lead to an increased incidence in the first round. It is, therefore, misleading to include the first round when studying incidence/overdiagnosis. Noninvasive cancer was included in the Malmö trial,3 while it was not in our study. Noninvasive cancer is more frequently detected in screening. We acknowledge the comment by Dr. Zahl and Prof. Maehlen about spontaneous regression. They made a comparison based on our figures, which showed that only a minor part (27%) of the difference between a screened and unscreened population can be explained by slow growing cancers. However, we believe that the figure is somewhat underestimated. There are two reasons for this. The attendance can be lower in women aged 70–74 years than in younger women. We do not know the attendance in all 11 counties in our study, but for the three counties where we know the figures, the attendance was 7–8% lower in 70–74 years than in 50–69 years. Women who had screen-detected tumor in age 50–69 that hypothetically would have been clinically detected after 70 years of age without screening and died before 70 years contribute to the figure 1648 but not to the figure 452. The probability of death between 50 and 69 is 10–15%. However, these remarks can only explain a minor part of the remaining cancers, and we agree that this may indicate that spontaneous regression may occur. However, the interpretation has to be careful because the estimation of expected incidence was based on historical data and we cannot exclude a break in the incidence trend because of changes in background factors contemporaneous with the introduction of service screening.
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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 | 0.015 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".