Breast cancer overdiagnosis in stop-screen trials: More uncertainty than previously reported
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: According to the Independent UK Panel on Breast Cancer Screening, the most reliable estimates of overdiagnosis for breast cancer screening come from stop-screen trials Canada 1, Canada 2, and Malmo. The screen-interval overdiagnosis fraction is the fraction of cancers in a screening program that are overdiagnosed. We used the cumulative incidence method to estimate screen-interval overdiagnosis fraction. Our goal was to derive confidence intervals for estimated screen-interval overdiagnosis fraction and adjust for refusers in these trials. METHODS: We first show that the UK Panel's use of a 95% binomial confidence interval for estimated screen-interval overdiagnosis fraction was incorrect. We then derive a correct 95% binomial-Poisson confidence interval. We also use the method of latent-class instrumental variables to adjust for refusers. RESULTS: For the Canada 1 trial, the estimated screen-interval overdiagnosis fraction was 0.23 with a 95% binomial confidence interval of (0.18, 0.27) and a 95% binomial-Poisson confidence interval of (0.04, 0.41). For the Canada 2 trial, the estimated screen-interval overdiagnosis fraction was 0.16 with a 95% binomial confidence interval of (0.12, 0.19) and a 95% binomial-Poisson confidence interval of (-0.01, 0.32). For the Malmo trial, the estimated screen-interval overdiagnosis fraction was 0.19 with a 95% binomial confidence interval of (0.15, 0.22). Adjusting for refusers, the estimated screen-interval overdiagnosis fraction was 0.26 with a 95% binomial-Poisson confidence interval of (0.03, 0.50). CONCLUSION: The correct 95% binomial-Poisson confidence interval s for the estimated screen-interval overdiagnosis fraction based on the Canada 1, Canada 2, and Malmo stop-screen trials are much wider than the previously reported incorrect 95% binomial confidence intervals. The 95% binomial-Poisson confidence intervals widen as follow-up time increases, an unappreciated downside of longer follow-up in stop-screen trials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it