Screening for Breast Cancer in 2018—What Should We be Doing Today?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although screening mammography has delivered many benefits since its introduction in Canada in 1988, questions about perceived harms warrant an up-to-date review. To help oncologists and physicians provide optimal patient recommendations, the literature was reviewed to find the latest guidelines for screening mammography, including benefits and perceived harms of overdiagnosis, false positives, false negatives, and technologic advances. For women 40-74 years of age who actually participate in screening every 1-2 years, breast cancer mortality is reduced by 40%. With appropriate corrections, overdiagnosis accounts for 10% or fewer breast cancers. False positives occur in about 10% of screened women, 80% of which are resolved with additional imaging, and 10%, with breast biopsy. An important limitation of screening is the false negatives (15%-20%). The technologic advances of digital breast tomosynthesis, breast ultrasonography, and magnetic resonance imaging counter the false negatives of screening mammography, particularly in women with dense breast tissue.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it