Overdiagnosis in Breast Cancer Chemoprevention Trials
Bibliographic record
Abstract
Several randomized controlled trials have demonstrated that the preventive use of an antiestrogen agent such as tamoxifen, raloxifene, anastrozole, or exemestane will reduce the incidence of estrogen receptor (er)–positive breast cancers by 50% or more. The reduction in risk becomes apparent shortly after tamoxifen initiation. However, no mortality benefit has yet been demonstrated with tamoxifen or any other agent, an effect that might be statistical: that is, the statistical power to detect a difference in mortality could be lacking because deaths from breast cancer are far fewer in number than cases of breast cancer, and because the average time to cancer is much shorter than the time to death. In other words, it could be too early to see an effect. However, the lack of an observed survival benefit might also be a result of chemoprevention agents preferentially preventing cancers that would rarely lead to death. That paradigm extends the (controversial) concepts of overdiagnosis and of the potential for spontaneous regression of some lowgrade breast cancers [...]
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 | 0.115 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".