Mammography Clinical Image Quality and the False Positive Rate in a Canadian Breast Cancer Screening Program
Bibliographic record
Abstract
PURPOSE: The study sought to determine if mammography quality is associated with the false positive (FP) rate in the Quebec breast cancer screening program in 2004 and 2005. METHODS: Mammography quality of a random sample of screen-film mammograms was evaluated by an expert radiologist following the criteria of the Canadian Association of Radiologists. For each screening examination, scores ranging from 1 (poor quality) to 5 (excellent quality) were attributed for positioning, compression, contrast, exposure level, sharpness, and artifacts. A final overall quality score (lower or higher) was also given. Poisson regression models with robust estimation of variance and adjusted for potential confounding factors were used to assess associations of mammography quality with the FP rate. RESULTS: Among 1,209 women without cancer, there were 104 (8.6%) FPs. Lower overall mammography quality is associated with an increase in the FP rate (risk ratio [RR], 1.4; 95% confidence interval [CI], 1.0-2.1; P = .07) but this increase was not statistically significant. Artifacts were associated with an increase in the FP rate (RR, 2.1; 95% CI, 1.3-3.3; P = .01) whereas lower quality of exposure level was related to a reduction of the FP rate (RR, 0.4; 95% CI, 0.1-1.0; P = .01). Lower quality scores for all other quality attributes were related to a nonstatistically significant increase in the FP rate of 10%-30%. CONCLUSIONS: Artifacts can have a substantial effect on the FP rate. The effect of overall mammography quality on the FP rate may also be substantial and needs to be clarified.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".