Medico-Legal Cases in Breast Imaging in Canada: A Trend Analysis
Bibliographic record
Abstract
Purpose: Breast imaging accounts for a large proportion of medico-legal cases involving radiologists in several countries and may be a disincentive to breast imaging. As this has not been well studied in Canada, we evaluated the key medico-legal issues of breast imaging in Canada and their implications for health care providers and patient safety. Methods: In collaboration with Canadian Medical Protective Association (CMPA), we obtained information from the medico-legal repository, including civil-legal, medical regulatory authority (College) and hospital complaints occurring between 2002-2021. Canadian Classification of Health Interventions (CCI) codes were used for breast imaging and biopsy. Trend analysis was done comparing cases involving breast imaging/biopsy to all cases where a radiologist was named. Results: Radiologists were named in 3108 medico-legal cases, 188 (6%, 188/3108) of which were CCI coded for breast imaging or biopsy. Factors related to radiologists were most frequent (64%, 120/188), followed by team (23.4%, 44/188) and system (6.9%, 13/188). Equal representation of male and female radiologists was found (IRR = 1.22; 95% CI: .89, 1.56). In a 10-year test window from 2006 - 2015 we identified an increasing trend for all cases involving radiologists ( P = 0,0128) but a decreasing trend for cases coded with breast imaging or biopsy ( P = 0,0099). Conclusions: A significant decrease in cases involving breast imaging were found from 2006-2015, accounting for 6% of the medico-legal cases. The lower risk of breast imaging medico-legal issues may encourage more radiologists in breast imaging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".