Quality control for digital mammography: Part II recommendations from the ACRIN DMIST trial
Bibliographic record
Abstract
The Digital Mammography Imaging Screening Trial (DMIST), conducted under the auspices of the American College of Radiology Imaging Network (ACRIN), is a clinical trial designed to compare the accuracy of digital versus screen-film mammography in a screening population [E. Pisano et al., ACRIN 6652-Digital vs. Screen-Film Mammography, ACRIN (2001)]. Part I of this work described the Quality Control program developed to ensure consistency and optimal operation of the digital equipment. For many of the tests, there were no failures during the 24 months imaging was performed in DMIST. When systems failed, they generally did so suddenly rather than through gradual deterioration of performance. In this part, the utility and effectiveness of those tests are considered. This suggests that after verification of proper operation, routine extensive testing would be of minimal value. A recommended set of tests is presented including additional and improved tests, which we believe meet the intent and spirit of the Mammography Quality Standards Act regulations to ensure that full-field digital mammography systems are functioning correctly, and consistently producing mammograms of excellent image quality.
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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 | 0.189 | 0.329 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.024 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".