Geographic variation and physician specialization in the use of percutaneous biopsy for breast cancer diagnosis.
Bibliographic record
Abstract
BACKGROUND: Preoperative diagnosis of breast cancer is a standard of care. We conducted a population-based study to determine the factors associated with the use of percutaneous needle biopsy to diagnose breast cancer in Ontario. METHODS: We identified a total of 3644 women who underwent breast tissue sampling (percutaneous needle biopsy or surgical excision) that yielded a diagnosis of cancer between Apr. 1, 2002, and Dec. 31, 2002, and for whom we were able to obtain complete data. We performed univariate and multivariate analyses to examine the association between a number of variables and the use of percutaneous biopsy or surgery for diagnosis and the performance of biopsy with or without image guidance. The variables were age, local health integration network (LHIN), income quintile, urban or rural residence, access to a primary care provider, prior mammogram, prior regular screening mammography, screen-initiated biopsy, and surgeon and radiologist specialization in breast disease. RESULTS: A total of 2374 women (65%) underwent percutaneous biopsy to diagnose breast cancer. The use of percutaneous biopsy varied from 22% to 81% among LHINs. On multivariate analysis, no patient variables were associated with the use of percutaneous biopsy for diagnosis. Only the LHIN and surgeon and radiologist specialization were predictive of whether a woman received a percutaneous biopsy. These 2 variables, along with income quintile and screen-initiated biopsy, were associated with the use of image-guided biopsy as the method of choice. CONCLUSION: Geographic variation in the use of percutaneous biopsy, particularly image-guided biopsy, for the diagnosis of breast cancer exists across Ontario. The frequency of such biopsies may be a useful quality indicator. Strategies to improve uptake of organized evidence-based care may increase the use of percutaneous biopsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".