Is carcinoma <i>in situ</i> a precursor lesion of invasive breast cancer?
Bibliographic record
Abstract
This study measures the probability of development of invasive breast cancer (BC) following the diagnosis of carcinomas in situ (CIS). A 25-year prospective follow-up was conducted by linking the Canadian National Breast Screening Study (CNBSS) to cancer registries and a national vital statistics database. Subsequent BC incidence was identified in CNBSS women who were diagnosed with CIS. CIS was classified into ductal (DCIS) and lobular carcinoma in situ (LCIS). Cumulative cancer incidence probabilities were calculated and a 1:5 matched nested case control study was conducted to estimate the odds of BC development. Of the 146 women diagnosed with CIS, 26 developed invasive BC (17.8%) and 12 died of BC (8.2%). The average time from the diagnosis of CIS to invasive BC was 6.3 years (± 5.6). The 20-year cumulative incidence probabilities for DCIS and LCIS were 19.0% (95%CI: 11.2, 26.8) and 21.3% (95%CI: 7.1, 35.4) respectively. The odds of development of BC in CIS women was significantly elevated compared with controls (OR = 2.6, 95% CI: 1.5, 4.5). While women with CIS had a higher odds of development of BC compared to those without CIS, at 20-year post CIS diagnosis, more than 80% of them remained free of invasive BC. This low probability of developing invasive BC post CIS diagnosis does not support the notion that CIS of the breast is an obligate precursor lesion of invasive BC.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".