The Burden of False-Positive Results in Analog and Digital Screening Mammography: Experience of the Nova Scotia Breast Screening Program
Bibliographic record
Abstract
PURPOSE: The Canadian Task Force on Preventive Health Care released recommendations for breast cancer screening, in part, based on harms associated with screening. The purpose of this study was to describe the rate of false-positive (FP) screening mammograms and to describe the extent of the investigations after an FP. METHODS: A cohort was identified that consisted of all screening mammograms performed through the Screening Program (2000-2011) with patients ages 40-69 years at screening. Rates of FP screening mammograms were calculated as well as rates of further investigations required, including additional imaging, needle core biopsy, and surgery. Analyses were stratified by 10-year age group, screening status (first vs rescreen), and technology. RESULTS: A total of 608,088 screening mammograms were included. The FP rate varied by age group, and decreased with increasing age (digital, 40-49 years old, FP = 8.0%; 50-59 years old, FP = 6.3%; 60-69 years old, FP = 4.6%). The FP rate also varied by screening status (digital, first screen, FP = 12.0%; rescreen, FP = 5.6%), and this difference was consistent across age groups. The need for further investigation varied by age group, with invasive procedures being more heavily used as women age (digital, rescreen group, surgery: 40-49 years old, 1.1%; 50-59 years old 1.6%, 60-69 years old, 1.8%). CONCLUSIONS: Both the FP screening mammogram rate and the degree to which further investigation was required varied by age group and screening status. Reporting on these rates should form part of the evaluation of screening performance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 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".