Digital Compared with Screen-Film Mammography: Performance Measures in Concurrent Cohorts within an Organized Breast Screening Program
Bibliographic record
Abstract
PURPOSE: To evaluate the performance of digital direct radiography (DR) and computed radiography (CR) compared with that of screen-film mammography (SFM) in large concurrent cohorts. MATERIALS AND METHODS: This study was approved by the University of Toronto Research Ethics Board and did not require informed consent. Concurrent cohorts of women aged 50-74 years screened with DR (n = 220 520), CR (n = 64 210), or SFM (n = 403 688) between 2008 and 2009 were identified and followed for 12 months. Performance was compared between cohorts, with SFM as the referent cohort. Associations were examined by using mixed-effect logistic regression. RESULTS: The cancer detection rate was similar for DR (4.9 per 1000; 95% confidence interval [CI]: 4.7, 5.2) and SFM (4.8 per 1000; 95% CI: 4.7, 5.0); however, the rate was significantly lower for CR (3.4 per 1000; 95% CI: 3.0, 3.9) (odds ratio, 0.79; 95% CI: 0.68, 0.93). Recall rates were higher for DR (7.7%; 95% CI: 7.6%, 7.8%) and lower for CR (6.6%; 95% CI: 6.5%, 6.7%) than for SFM (7.4%; 95% CI: 7.3%, 7.5%). Positive predictive value was lower for CR (5.2%; 95% CI: 4.7%, 5.8%) than for SFM (6.6%; 95% CI: 6.4%, 6.8%); however, the adjusted odds were not significant. CONCLUSION: Although DR is equivalent to SFM for breast screening among women aged 50-74 years, the cancer detection rate was lower for CR. Screening programs should monitor the performance of CR separately and may consider informing women of the potentially lower cancer detection rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".