Improving Work-Up of the Abnormal Mammogram Through Organized Assessment: Results From the Ontario Breast Screening Program
Bibliographic record
Abstract
PURPOSE: Women with an abnormal screening mammogram should ideally undergo an organized assessment to attain a timely diagnosis. This study evaluated outcomes of women undergoing work-up after abnormal mammogram through a formal breast assessment affiliate (BAA) program with explicit care pathways compared with usual care (UC) using developed quality indicators for screening mammography programs. METHODS: Between January 1 and December 31, 2007, a total of 320,635 women underwent a screening mammogram through the Ontario Breast Screening Program (OBSP), of whom 25,543 had an abnormal result requiring further assessment. Established indicators assessing timeliness, appropriateness of follow-up, and biopsy rates were compared between women who were assessed through either a BAA or UC using χ(2) analysis. RESULTS: Work-up of the abnormal mammogram for patients screened through a BAA resulted in a greater proportion of women attaining a definitive diagnosis within the recommended time interval when a histologic diagnosis was required. In addition, use of other quality measures including specimen radiography for both core biopsies and surgical specimens and preoperative core needle biopsy was greater in BAA facilities. CONCLUSION: These findings support future efforts to increase the number of BAAs within the OBSP, because the pathways and reporting methods associated with them result in improvements in our ability to provide timely and appropriate care for women requiring work-up of an abnormal mammogram.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".