American College of Radiology Imaging Network Digital Mammographic Imaging Screening Trial: Objectives and Methodology
Bibliographic record
Abstract
This study was approved by the Institutional Review Board (IRB) of the American College of Radiology Imaging Network (ACRIN) and each participating site and by the IRB and the Cancer Therapy Evaluation Program at the National Cancer Institute. The study was monitored by an independent Data Safety and Monitoring Board, which received interim analyses of data to ensure that the study would be terminated early if indicated by trends in the outcomes. The ACRIN, which is funded by the National Cancer Institute, conducted the Digital Mammographic Imaging Screening Trial (DMIST) primarily to compare the diagnostic accuracy of digital and screen-film mammography in asymptomatic women presenting for screening for breast cancer. Over the 25.5 months of enrollment, a total of 49 528 women were included at the 33 participating sites, which used five different types of digital mammography equipment. All participants underwent both screen-film and digital mammography. The digital and screen-film mammograms of each subject were independently interpreted by two radiologists. If findings of either examination were interpreted as abnormal, subsequent work-up occurred according to the recommendations of the interpreting radiologist. Breast cancer status was determined at biopsy or follow-up mammography 11-15 months after study entry. In addition to the measurement of diagnostic accuracy by using the interpretations of mammograms at the study sites, DMIST included evaluations of the relative cost-effectiveness and quality-of-life effects of digital versus screen-film mammography. Six separate reader studies using the de-identified archived DMIST mammograms will also assess the diagnostic accuracy of each of the individual digital mammography machines versus screen-film mammography machines, the effect of breast density on diagnostic accuracy of digital and screen-film mammography, and the effect of different rates of breast cancer on the diagnostic accuracy in a reader study.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".