On the benefits and harms of screening for breast cancer
Bibliographic record
Abstract
claim that our critique of the randomized screening trials has little merit; that there is no reason to believe that the Canadian study was of better quality than the New York Health Insurance Plan (HIP) study or the Two-County study; and that the prior consensus on mammography was correct. However, their review suffers from erroneous assumptions and biased statistical analyses, and their quotations are often selective and misleading. In my discussion of the issues, I will follow when possible the sequence of arguments used by FPR. Overdiagnosis and overtreatment FPR claim in their abstract that early detection leads to less invasive therapy. This could have been true, if the only effect of screening had been to detect the same tumours earlier that are detected later if women are not screened. FPR naively believe that screening does just that, i.e. does not lead to overdiagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.124 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".