Evaluation of breast cancer screening programmes: Candidate performance indicators and their association with breast cancer mortality
Bibliographic record
Abstract
AIM: Evaluation of a breast cancer (BC) screening programme is necessary to ensure its quality. Performance measurements might be prioritized considering the association with outcomes related to BC mortality. We piloted an approach to explore the association of selected performance measurements with incidence-based BC mortality (IBM). METHODS: We performed an ecological analysis of aggregated data from regional or national population-based cancer registries and BC screening programmes in Europe, using 13 performance indicators. We built a panel data (longitudinal cross-sectional) regression model to estimate the association between screening performance measurements and IBM rates. RESULTS: We included data of 9 programmes and registries from Italy, Spain, Norway, Ireland and the Czech Republic. The number of screening years included in the dataset ranged from 5 to 20 years. In adjusted panel analyses, higher screening coverage, breast cancer detection rates (BCDR prevalent and subsequent rounds), node-negative proportion, and episode sensitivity were associated with lower incidence-based mortality (IBM), whereas a higher interval cancer rate was associated with higher IBM. The association for recall rate in subsequent examinations was small and imprecise. CONCLUSION: Our pilot approach suggests association of several performance indicators with IBM. These indicators were related to the implementation of the screening programme (screening coverage), sensitivity (BC detection rate), and efficiency (recall rate). Further studies with larger datasets and individual data may confirm these findings.
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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 | 0.000 |
| 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".