Bibliographic record
Abstract
Ultrasound is comparable to mammography in detecting breast cancer and should be considered when testing for the disease, say the authors of a study published in the Journal of the National Cancer Institute.1 Mammography is not commonly available in developing nations, and alternative methods such as ultrasound need to be tested, according to the authors. Researchers recruited 2809 women across 20 different sites in the United States, Canada, and Argentina to the American College of Radiology Imaging Network protocol 6666 study of breast cancer screening. Of the participants, 2662 completed 3 annual breast screenings by ultrasound and film-screen or digital mammography and underwent a biopsy or a 12-month follow-up. Findings indicated that the number of ultrasound screenings was comparable to that of mammography and that there was a greater percentage of invasive and lymph node-negative cancers diagnosed among those who had undergone ultrasound; however, the study also reported a greater number of false-positive results among women screened with ultrasound. Nevertheless, the authors add that the number of women recalled for additional testing became more comparable to mammography on incidence screening rounds, and they suggest that ultrasound be considered a supplemental test for women with dense breasts who do not meet high-risk criteria for magnetic resonance imaging screening and for high-risk women with dense breasts who are unable to tolerate magnetic resonance imaging.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".