Radiologist Interpretive Volume and Breast Cancer Screening Accuracy in a Canadian Organized Screening Program
Bibliographic record
Abstract
BACKGROUND: To strengthen evidence on which radiologist mammography interpretive volume requirements can be based, we assessed the relation of volume to accuracy in the Quebec Breast Cancer Screening Program. METHODS: Annual interpretive volume (total, screening, and diagnostic) for all 340 radiologists who interpreted 1315327 screening examinations in the period from 2000 to 2006 was obtained using provincial databases. The association of volume to sensitivity, false-positive rate, and accuracy (sensitivity/false-positive rate) was assessed by multivariable Poisson regression with robust error variance. All statistical tests were two-sided. RESULTS: Radiologists consistently interpreting less than 500 mammograms annually experienced a 58% reduction in accuracy (adjusted accuracy ratio = 0.42; 95% confidence interval [CI] = 0.24 to 0.74) compared with those who consistently interpreted at least 500 mammograms annually. Moreover, accuracy increased progressively as total annual volume increased (P trend = .0005). Radiologists interpreting at least 4000 mammograms annually experienced a 32% increase in accuracy (adjusted accuracy ratio = 1.32; 95% CI = 1.13 to 1.54) compared with those interpreting 500 to 999 mammograms annually. This increase in accuracy is attributable to a reduction in false-positive rate as total volume increased (P trend = .001). Sensitivity changed little with total volume (P trend = .68). Gains in accuracy were greater up to approximately 3000 mammograms interpreted annually. CONCLUSIONS: The minimum annual volume of 500 mammograms required in North America is justified; radiologist accuracy may be compromised if interpretive volume is consistently less than this requirement. Raising interpretive volume may help to reduce the frequency of false positives without loss of sensitivity. Possible gains in accuracy may be greater with increases in volume of up to approximately 3000 mammograms interpreted annually.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".