Frequency and Determinants of a Short-Interval Follow-up Recommendation after an Abnormal Screening Mammogram
Bibliographic record
Abstract
PURPOSE: After imaging assessment of an abnormal screening mammogram, a follow-up examination 6 months later is recommended to some women. Our aim was to identify which characteristics of lesions, women, and physicians are associated to such short-interval follow-up recommendation in the Quebec Breast Cancer Screening Program. METHODS: Between 1998 and 2008, 1,839,396 screening mammograms were performed and a total of 114,781 abnormal screens were assessed by imaging only. Multivariate analysis was done with multilevel Poisson regression models with robust variance and generalized linear mixed models. RESULTS: A short-interval follow-up was recommended in 26.7% of assessments with imaging only, representing 2.3% of all screens. Case-mix adjusted proportion of short-interval follow-up recommendations varied substantially across physicians (range: 4%-64%). Radiologists with high recall rates (≥15%) had a high proportion of short-interval follow-up recommendation (risk ratio: 1.82; 95% confidence interval: 1.35-2.45) compared to radiologists with low recall rates (<5%). The adjusted proportion of short-interval follow-up was high (22.8%) even when a previous mammogram was usually available. CONCLUSIONS: Short-interval follow-up recommendation at assessment is frequent in this Canadian screening program, even when a previous mammogram is available. Characteristics related to radiologists appear to be key determinants of short-interval follow-up recommendation, rather than characteristics of lesions or patient mix. Given that it can cause anxiety to women and adds pressure on the health system, it appears important to record and report short-interval follow-up and to identify ways to reduce its frequency. Short-interval follow-up recommendations should be considered when assessing the burden of mammography screening.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".