Tumor size distribution of invasive breast cancers and the sensitivity of screening methods in the Canadian National Breast Screening Study
Bibliographic record
Abstract
INTRODUCTION: This study set out to explore if breast cancers of different sizes are detected with varying sensitivity. In addition, we attempt to determine the effect of tumor size on screening detectability. SUBJECTS AND METHODS: Data arising from the Canadian National Breast Screening Study (CNBSS) was used to perform all analyses. The CNBSS consists of two randomized controlled trials, which includes data on detection methods, age, and allocation groups. We stratified tumor size by 5 mm; age into 40-49 and 50-59 years age groups; and cancer detection or presentation methods into mammography only, physical breast examination only, both mammography and physical breast examination, interval cancers, and incident cancers. RESULTS: This study revealed that a difference in tumor size exists for age (smaller tumor sizes are found in older women) and breast cancer detection or presentation modes. More specifically, breast cancers detected by mammography screening are statistically smaller than those detected by physical breast examination or those presenting as incident or interval cancers. This study also found that tumor size affects screening detectability for women in their 50's but not in their forties. That is, a statistically significant difference between mammography screening alone and physical examination alone was observed for women between the ages of 50-59 for tumor sizes up to 20 mm, including prevalent cases, and up to 15 mm when prevalent cases were excluded. CONCLUSION: The results of this study suggest that smaller breast cancers are more likely to be detected among women in their 50s.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".