Canadian National Breast Screening Study (CNBSS): Mortality (MOR), survival (SUR), and mammographic screening (MS).
Bibliographic record
Abstract
e12614 Background: Among randomized trials on MS for breast cancer (BC), CNBSS is most controversial. Original CNBBS publications (CMAJ, 1992) suggest that MS may be detrimental to women age 40-49 and 50-59 yrs. Notwithstanding, CNBSS investigators interpret results in a recent paper (BMJ, 2014) as supporting that MS leads to BC overdiagnosis (OD). Objective here is to investigate the hypothesis that randomization (RAN) failure confounded interpretation of CNBSS. Methods: CNBSS1 compared annual MS and clinical breast examination (CBE) to a single CBE in women 40-49 yrs. CNBSS2 compared MS+CBE to CBE in women 50-59 yrs. We performed statistical analyses on published CNBSS data. Results: In CNBSS1, after 7 yrs, there were more invasive BCs in experimental group (EG) (RR = 1.2; p = 0.02). There were also more BCs with > 4+ nodes on prevalence screen (RR = 3.4; p = 0.01) & after 7 yrs (RR = 2.0; p = 0.004). While BC MOR was higher in EG (RR = 1.4; p = 0.3), higher MOR relates to higher incidence (INC) of poor prognosis BCs in EG. In CNBSS2, randomization failure appears to have contributed to negative results; eg BC INC (RR = 1.8; p = 0.001) and MOR (RR = 2.1; p = 0.047) were significantly higher in EG in prevalence screen. In their 2014 report, CNBSS investigators pool results of both studies. Among BCs detected during 5-yr experimental period, INC was 27% higher in EG (666 vs. 524, p < 0.0001). After 25 yrs, there were 180 vs. 171 BC deaths among these cases (RR = 1.1; p = 0.7). While MOR slightly favors controls, SUR was significantly superior in EG: 25-yr SUR was 71% in EG vs 63% in CG (RR = 0.8; p = 0.02). Among EG women whose cancer was detected by MS, 25-yr SUR was 80%. CNBSS investigators conclude that OD accounts for SUR/MOR discrepancy. Conclusions: OD does not account for SUR/MOR discrepancy in CNBSS. Higher rates of poor prognosis BCs in EG support that RAN failed to provide comparison groups with an equal probability of BC MOR. MOR comparisons do not provide an accurate measure of MS efficacy in CNBSS. Problems with RAN more plausibly account for these findings. The 2014 CNBSS report provides little evidence about MS effectiveness. Nonetheless, superior long-term SUV in EG supports that MS improved outcome for those randomized to MS in 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".