A Retrospective Chart Analysis Comparing Breast Cancer Detection Rates Between Annual Versus Biennial Mammograms
Bibliographic record
Abstract
Background: Per American Cancer Society, breast cancer is one of the most prevalent causes of cancer-related mortality in women in the United States. Different organizations vary in their recommendations regarding frequency of mammograms, with the United State Preventive Service Taskforce recommending biennial screening and other organizations like American College of Radiology promoting annual screening. The purpose of this study was to analyze institutional data to compare breast cancer detection rates among women undergoing annual vs. biennial mammograms. Methods: In this retrospective chart review, we analyzed deidentified records of women aged 25 to 74 at Northeast Georgia Health System, who had undergone at least two screening mammograms and were diagnosed with primary breast cancer. We analyzed several variables including Breast Imaging Reporting and Data System (BI-RADS) categorization, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, age, race, ethnicity, nodal involvement, smoking status, insurance status, grade, tumor size, number of screening mammograms, personal history of breast cancer, family history of breast cancer, and their correlation to screening frequency (annual vs. biennial vs. less than biennial). Results: Among the total 2,219 records that satisfied the inclusion criteria, we observed that BI-RADS categorization (P < 0.001), ER status (P = 0.003), and PR status (P = 0.001) were associated with mammogram screening frequency while the other variables were not statistically significant. Post-hoc analysis revealed that biennially screened patients exhibited less N2 node involvement than expected (P = 0.022). Additionally, Hispanic/Latino(a) patients had a greater frequency of biennial screenings than expected (P = 0.050). Lastly, post-hoc analysis revealed that current smokers had a greater incidence of less-frequent-than-biennial screenings (P = 0.023). Conclusions: Annual mammograms were associated with a lower BI-RADS stage and lower stage of breast cancer diagnosis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".