Annual vs Biennial Screening: Diagnostic Accuracy Among Concurrent Cohorts Within the Ontario Breast Screening Program
Bibliographic record
Abstract
BACKGROUND: The Ontario Breast Screening Program recommends annual mammography to women age 50-74 years at increased risk because of family history of breast or ovarian cancer or personal history of ovarian cancer or mammographic density 75% or greater. Few studies have examined the diagnostic accuracy of recommendations based on risk factors and included screen film as well as digital mammography. METHODS: A retrospective design identified concurrent cohorts of women age 50-74 years screened annually or biennially with digital mammography only between 2011 and 2014 and followed until 2016 or breast cancer diagnosis. Diagnostic accuracy measures were compared between women screened annually because of first-degree relative of breast or ovarian cancer or personal history of ovarian cancer (n = 67 795 women), mammographic density 75% or greater (n = 51 956), or both (n = 3758) and those screened biennially (n = 526 815). The association between recommendation and sensitivity and specificity was assessed using generalized estimating equation models. All P values are two-sided. RESULTS: For annual screening because of family or personal history vs biennial, sensitivity was statistically significantly higher (81.7% vs 70.6%; OR = 1.86, 95% CI = 1.48 to 2.34), particularly for invasive cancers and postmenopausal women. Although there was no statistically significant difference in sensitivity for annual screening for mammographic density 75% or greater, specificity was statistically significantly lower (91.3%; OR = 0.87, 95% CI = 0.80 to 0.96) vs biennial (92.3%), particularly for women age 50-59 years. CONCLUSION: Compared with biennial screening, annual screening improved detection for women with a family or personal history of breast and/or ovarian cancer, supporting screening that is more frequent. The benefit for annual screening for women with higher mammographic density must be weighed against possible harms of increased false positives.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".