Volume of screening mammography and performance in the Quebec population-based Breast Cancer Screening Program
Bibliographic record
Abstract
BACKGROUND: In the Quebec Breast Cancer Screening Program (Programme quebecois de depistage du cancer du sein [PQDCS]), radiologists' and facilities' volumes of screening mammography vary considerably. We examined the relation of screening-mammography volume to rates of breast cancer detection and false-positive readings in the PQDCS. METHODS: The study population included 307,314 asymptomatic women aged 50-69 years screened during 1998-2000. Breast cancer detection rates were analyzed by comparing all women with screening-detected breast cancer (n = 1709) and a 10% random sample of those without (n = 30,560). False-positive rates were analyzed by comparing the 3159 women with false-positive readings and the 27,401 others in the 10% random sample. Characteristics of participants, radiologists and facilities were obtained from the PQDCS information system. Data were analyzed by means of logistic regression. RESULTS: The rate of breast cancer detection appeared to be unrelated to the radiologist's screening-mammography volume but increased with the facility's screening-mammography volume. The breast cancer detection rate ratio for facilities performing 4000 or more screenings per year, compared with those performing fewer than 2000, was 1.28 (95% confidence interval [CI] 1.07-1.52). In contrast, the frequency of false-positive readings was unrelated to the facility's screening volume but was inversely related to the radiologist's screening volume: the rate ratio for readers of 1500 or more screenings per year compared with those reading fewer than 250 was 0.53 (95% CI 0.35-0.79). INTERPRETATION: Radiologists' and facilities' caseloads showed independent and complementary associations with performance of screening mammography in the PQDCS. Radiologists who worked in larger facilities and read more screening mammograms had higher breast cancer detection rates while maintaining lower false-positive 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".