Major failings of trial procedures and quality of screening fatally compromise the results of the Canadian National Breast Screening Studies
Bibliographic record
Abstract
Despite overwhelming evidence of a major reduction in deaths, the debate about the efficacy of breast cancer screening has continued for over 50 years. The poor results in the Canadian National Breast Screening Studies (CNBSS) have been used to challenge the benefits shown by the other randomized, controlled trials. They continue to be used in assessing the value of breast cancer screening despite their unblinded allocation process, which first identified women with breast abnormalities and then assigned them on open lists allowing for nonrandom assignment, compromising the trials and rendering their results unreliable. There were, statistically significantly, more women with advanced cancers who were assigned to the screening arm in CNBSS1. The early results for CNBSS1 showed an excess of women dying in the screening arm, and an (otherwise inexplicable) greater than 90%, 5-year survival for the control women. The failure of random assignment also explains why the clinically evident cancers were larger in the screening arms than the cancers in the "usual care" arms, despite the fact that the screened women underwent very intense clinical breast examinations each year by highly skilled examiners. The claim that balanced demographic factors prove random assignment is also false. Nonrandom allocation of a hundred or more women with clinically evident abnormalities would have no detectable influence on the distribution of demographic factors. In summary, policy decisions about mammography should not be influenced by the results of the CNBSS.
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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 | 0.764 | 0.886 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".