Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
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
Previous trials, limited in many respects, have not found digital mammography to be significantly more accurate than the standard film method. A total of 42,760 asymptomatic women seen at 33 sites in the United States and Canada requested screening mammography and underwent both film and digital examinations. Two radiologists independently interpreted the film and digital mammograms. All participants either had breast biopsy within 15 months after evaluation or had a follow-up mammogram 10 months or longer after entry to the study. The results were assessed by receiver operating characteristic analysis. Both digital and film mammograms were positive in 0.5% of women. Another 2.2% had only a positive digital study, whereas 1.9% had only a positive film study. In the remaining women, approximately 95% of the total, both imaging studies were negative. Of 335 breast cancers diagnosed within 455 days after entry to the study, approximately three fourths were found within a year after evaluation. There were no substantial differences between the digital and film findings with respect to histology or stage of disease. The area under the curve was similar for the 2 studies and was not influenced by race or the risk of breast cancer. Digital mammography did, however, perform significantly better than the film method in women less than 50 years of age, in those having heterogeneously dense or very dense breasts, and premenopausal or perimenopausal women. The digital and film methods performed equally well in women age 50 years and older, those with fatty breasts or scattered fibroglandular densities, and those who were postmenopausal.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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