Standardized Abnormal Interpretation and Cancer Detection Ratios to Assess Reading Volume and Reader Performance in a Breast Screening Program
Bibliographic record
Abstract
PURPOSE: To determine the relationship between annual screening volume and radiologist performance in the Screening Mammography Program of British Columbia, Canada. MATERIALS AND METHODS: Standardized abnormal interpretation ratios and standardized cancer detection ratios were constructed for 35 readers with at least 3 years of experience with the Screening Mammography Program of British Columbia. The ratios were used to compare individual reader performance with the mean program performance after adjustment for the age and screening history (first versus subsequent screening examinations) of the women who underwent screening. RESULTS: The mean standardized abnormal interpretation ratio was better for readers of 2,000-2,999 (n = 8) and 3,000-3,999 (n = 9) screening mammograms per year than for those of less than 2,000 (n = 9) and 4, 000-5,199 (n = 9) screening mammograms per year. Differences in the mean standardized abnormal interpretation ratios were significant (P <.05) between the readers of less than 2,000 and of 2,000-2,999 screening mammograms per year, between readers of less than 2,000 and of 3,000-3,999 screening mammograms per year and between readers of 3,000-3,999 and of 4,000-5,199 screening mammograms per year. The mean standardized cancer detection ratio improved gradually with increasing annual volume, but the differences between groups were not statistically significant. Five of the eight readers of 2,000-2, 999 mammograms were reading 2,475 or more screening mammograms per year. CONCLUSION: Standardized abnormal interpretation ratios and standardized cancer detection ratios provide a method of comparing two important performance measures in a screening program. A minimum of 2,500 interpretations per year is associated with lower abnormal interpretation rates and average or better cancer detection rates.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".