Clinical Image Quality and Sensitivity in an Organized Mammography Screening Program
Bibliographic record
Abstract
PURPOSE: The study sought to examine the association between clinical image quality of mammograms and screening sensitivity. METHODS: Four radiologists evaluated the clinical image quality of 374 invasive screen-detected cancers and 356 invasive interval breast cancers for which quality evaluation of screening mammograms could be assessed from cancers diagnosed among participants in the Quebec Breast Cancer Screening Program in 2007. Quality evaluation was based on the Canadian Association of Radiologists accreditation criteria, which are similar to those of the American College of Radiology. The association between clinical quality and screening sensitivity was assessed by logistic regression. Adjusted sensitivity and adjusted sensitivity ratios were obtained through marginal standardization. No institutional review board approval was required. RESULTS: A proportion of 28% (206 of 730) of screening mammograms had lower overall quality for the majority of assessments. Positioning was the quality attribute that was the most frequently deficient. The 2-year screening sensitivity reached 68%. Sensitivity of screening was not statistically associated with the overall quality (ratio of 2-year sensitivity = 1.03; 95% confidence interval: 0.93-1.15) or with any quality attributes (positioning, exposure, compression, sharpness, artifacts, contrast). Results were similar for the 1-year sensitivity. CONCLUSIONS: Although not all mammograms in the Quebec screening program met the optimum quality required by the Canadian Association of Radiologists or American College of Radiology accreditation, the screening mammograms produced in this population-based organized screening program reached a high enough level of quality so that the remaining variation in quality is too little to impair screening sensitivity.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".