Bibliographic record
Abstract
Screening for breast cancer has been evaluated by 9 randomized trials over 5 decades and recommended by major guideline groups for more than 3 decades. Successes and lessons for cancer screening from this history include development of scientific methods to evaluate screening, by the Canadian Task Force on the Periodic Health Examination and the U.S. Preventive Services Task Force; the importance of randomized trials in the past, and the increasing need to develop new methods to evaluate cancer screening in the future; the challenge of assessing new technologies that are replacing originally evaluated screening tests; the need to measure false-positive screening test results and the difficulty in reducing their frequency; the unexpected emergence of overdiagnosis due to cancer screening; the difficulty in stratifying individuals according to breast cancer risk; women's fear of breast cancer and the public outrage over changing guidelines for breast cancer screening; the need for population scientists to better communicate with the public if evidence-based recommendations are to be heeded by clinicians, patients, and insurers; new developments in the primary prevention of cancers; and the interaction between improved treatment and screening, which, over time, and together with primary prevention, may decrease the need for cancer screening.
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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".