Comments on “Finding the optimal mammography screening strategy: A cost‐effectiveness analysis of 920 modeled strategies”
Bibliographic record
Abstract
The validity of the model estimating that biennial breast cancer screening for women aged 50 to 74 will avert 16 breast cancer deaths and provide 231 QALYs per 1000 women compared to no screening seems far too optimistic.1 The use of modeling-data studies to expand the age range for screening seems a desperate run forward flying in the face of the evidence. Analyses of two different trials—the Age trial and the Canadian National Breast Screening Study-I (CNBSS-I) for women aged 40 to 49 years showed no significant effect on breast cancer mortality and while overdiagnosis was estimated to be 32% at 5 years postcessation of screening.6 Randomized trials and adequate case-control studies provide persuasive evidence, they are preferable to modeling uncertain data using complex processes. Screening advocates should use modeling data studies to implement the use of personalized benefit/harm ratio, a prerequisite for true shared decision making. The issue is not screening more by expanding the age range in lower risk population, even more as uptake is likely to be lower in youngest populations, but screening better by improving: (a) compliance for long term uptake in those most at risk; (b) quality, in the Netherlands 21% of screen detected cancers and 24% of interval cancers were considered to be missed at a previous screen.7 The authors declare no conflicts of interest.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.044 | 0.030 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".