Advanced Breast Cancer and Breast Cancer Mortality in Randomized Controlled Trials on Mammography Screening
Bibliographic record
Abstract
PURPOSE: We assessed changes in advanced cancer incidence and cancer mortality in eight randomized trials of breast cancer screening. PATIENTS AND METHODS: Depending on published data, advanced cancer was defined as cancer > or = 20 mm in size (four trials), stage II+ (four trials), and > or = one positive lymph node (one trial). For each trial, we obtained the estimated relative risk (RR) and 95% CI between the intervention and control groups, for both breast cancer mortality and diagnosis of advanced breast cancer. Using a meta-regression approach, log(RR-mortality) was regressed on log(RR-advanced cancer), weighting each trial by the reciprocal of the square of the standard error of log(RR) for mortality. RESULTS: RR for advanced breast cancer ranged from 0.69 (95% CI, 0.61 to 0.78) in the Swedish Two-County Trial to 0.97 (95% CI, 0.97 to 1.25) in the Canadian National Breast Screening Study-1 (NBSS-1) trial. Log(RR)s for advanced cancer were highly predictive of log(RR)s for mortality (R(2) = 0.95; P < .0001), and the linear regression curve had a slope of 1.00 (95% CI, 0.76 to 1.25) after fixing the intercept to zero. The slope changed only slightly after excluding the Two-County Trial and the Canadian NBSS-1 and NBSS-2 trials. CONCLUSION: In trials on breast cancer screening, for each unit decrease in incidence of advanced breast cancer, there was an equal decrease in breast cancer mortality. Monitoring of incidence of advanced breast cancer may provide information on the current impact of screening on breast cancer mortality in the general population.
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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 | 0.050 | 0.127 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".