Bibliographic record
Abstract
Many women remain unaware of the extent to which efforts to achieve breast cancer control through mammography screening may be doing harm as well as good. An unacknowledged harm is that for up to 11 years after the initiation of breast cancer screening in women aged 40–49 years, screened women face a higher death rate from breast cancer than unscreened control women, although that is contrary to what one would expect (1). The belief in early detection as a universal good is widespread among women, health care professionals, and the media, all of whom focus on the benefits from mammography screening. Although the bad news about excess breast cancer deaths was published in the Journal (1), in 6 years it has been cited only eight times, four of which were by the same research group. Discussing the bad news is considered unethical and alarmist. Recently, the U.S. Preventive Services Task Force ignored the issue (2); in contrast, the International Agency for Research on Cancer acknowledged it (3). The truth about mammography may be that early detection is often but not always beneficial for women who are screened. The truth may be that early adverse outcomes for some premenopausal women (4) are only later counterbalanced by beneficial outcomes for others in the age group 40–49 years. Table 1 presents an overview of the results from the two Canadian trials (5,6) and the recent meta-analysis of five Swedish trials (7). The meta-analysis, a response to earlier criticism (8), presents the best available analysis of the Swedish screening trials. The Edinburgh trial is excluded from Table 1 because of problems with randomization (2). With the exception of results for women aged 60–69 years, Table 1 reveals that the reduction in deaths from breast cancer achieved by screening is modest. Because the rate ratios are reported after extended follow-up, the mortality paradox is not apparent in the table.
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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 | 0.128 | 0.488 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.026 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".