Is the false-positive rate in mammography in North America too high?
Bibliographic record
Abstract
The practice of investigating pathological abnormalities in the breasts of females who are asymptomatic is primarily employed using X-ray mammography. The importance of breast screening is reflected in the mortality-based benefits observed among females who are found to possess invasive breast carcinoma prior to the manifestation of clinical symptoms. It is estimated that population-based screening constitutes a 17% reduction in the breast cancer mortality rate among females affected by invasive breast carcinoma. In spite of the significant utility that screening confers in those affected by invasive cancer, limitations associated with screening manifest as potential harms affecting individuals who are free of invasive disease. Disease-free and benign tumour-bearing individuals who are subjected to diagnostic work-up following a screening examination constitute a population of cases referred to as false positives (FPs). This article discusses factors contributing to the FP rate in mammography and extends the discussion to an assessment of the consequences associated with FP reporting. We conclude that the mammography FP rate in North America is in excess based upon the observation of overtreatment of in situ lesions and the disproportionate distribution of detriment and benefit among the population of individuals recalled for diagnostic work-up subsequent to screening. To address the excessive incidence of FPs in mammography, we investigate solutions that may be employed to remediate the current status of the FP rate. Subsequently, it can be suggested that improvements in the breast-screening protocol, medical litigation risk, image interpretation software and the implementation of image acquisition modalities that overcome superimposition effects are promising solutions.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".