Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
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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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".